Triple
T3984632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rutland |
E86840
|
entity |
| Predicate | vehicleRegistrationCodeHistoric |
P25395
|
FINISHED |
| Object |
RU
RU is the historic vehicle registration code that was used for the English county of Rutland.
|
E404871
|
NE FINISHED |
How this triple was built (5 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: RU | Statement: [Rutland, vehicleRegistrationCodeHistoric, RU]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RU Context triple: [Rutland, vehicleRegistrationCodeHistoric, RU]
-
A.
RU
RU is the common abbreviation for Rutgers University, a major public research institution in New Jersey.
-
B.
RU
RU is the two-letter ISO 3166 country code for the Russian Federation.
-
C.
RU
RU is the common abbreviation for Radboud University Nijmegen, a major research university located in Nijmegen, the Netherlands.
-
D.
RUS
RUS is the acronym for the Rural Utilities Service, a U.S. government agency that provides funding and support for rural infrastructure such as electricity, water, and telecommunications.
-
E.
RU-KOS
RU-KOS is the ISO 3166-2 subdivision code assigned to Kostroma Oblast, a federal subject in central Russia.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: RU Triple: [Rutland, vehicleRegistrationCodeHistoric, RU]
Generated description
RU is the historic vehicle registration code that was used for the English county of Rutland.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: RU Target entity description: RU is the historic vehicle registration code that was used for the English county of Rutland.
-
A.
RU
RU is the common abbreviation for Rutgers University, a major public research institution in New Jersey.
-
B.
RU
RU is the two-letter ISO 3166 country code for the Russian Federation.
-
C.
RU
RU is the common abbreviation for Radboud University Nijmegen, a major research university located in Nijmegen, the Netherlands.
-
D.
RUS
RUS is the acronym for the Rural Utilities Service, a U.S. government agency that provides funding and support for rural infrastructure such as electricity, water, and telecommunications.
-
E.
RU-KOS
RU-KOS is the ISO 3166-2 subdivision code assigned to Kostroma Oblast, a federal subject in central Russia.
- F. None of above. chosen
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vehicleRegistrationCodeHistoric Context triple: [Rutland, vehicleRegistrationCodeHistoric, RU]
-
A.
hasHistoricRegistrationPlateCode
chosen
Indicates that an entity is associated with a specific code assigned to its historic (vintage/classic) vehicle registration plate.
-
B.
vehicleRegistrationCode
Indicates the official registration identifier assigned to a vehicle, typically used for legal identification and record-keeping.
-
C.
identifiesVehiclesRegisteredIn
Indicates that an entity specifies or determines which vehicles are registered within a particular scope or authority.
-
D.
hasTransportHistory
Indicates that there exists a record or sequence of past transportation-related events or movements associated with an entity.
-
E.
hasTransportHistoryAs
Indicates that an entity has a record or log of being transported, characterized or classified in a specific way.
- F. None of above.
Provenance (6 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69aed93fd9d4819085d3b2137d2346cb |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefa3ef7ac8190abe02f440ff83c43 |
completed | March 9, 2026, 4:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b540284d548190821d37b68974a2d2 |
completed | March 14, 2026, 11:02 a.m. |
| NEDg | Description generation | batch_69b54126234c8190990cf6df84a83e92 |
completed | March 14, 2026, 11:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5458d736481908ca826fa4fd01b8c |
completed | March 14, 2026, 11:25 a.m. |
| PD | Predicate disambiguation | batch_69aef8f492ac819089dbb9436dbcdd2b |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:33 p.m.