Triple
T8974087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liteyny Municipal Okrug |
E214339
|
entity |
| Predicate | hasRegionalCode |
P3446
|
FINISHED |
| Object |
RU-SPE
RU-SPE is the regional code for Saint Petersburg, a federal city and major cultural and economic center in northwestern Russia.
|
E769183
|
NE FINISHED |
How this triple was built (4 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-SPE | Statement: [Liteyny Municipal Okrug, hasRegionalCode, RU-SPE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RU-SPE Context triple: [Liteyny Municipal Okrug, hasRegionalCode, RU-SPE]
-
A.
Rus
Rus was a medieval East Slavic cultural and political realm that laid the foundations for the modern nations of Russia, Ukraine, and Belarus.
-
B.
RU
RU is the common abbreviation for Rutgers University, a major public research institution in New Jersey.
-
C.
RU
RU is the historic vehicle registration code that was used for the English county of Rutland.
-
D.
RU
RU is the common abbreviation for Radboud University Nijmegen, a major research university located in Nijmegen, the Netherlands.
-
E.
RU
RU is the commonly used abbreviation for the University of Rajshahi, a major public university in Bangladesh.
- 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-SPE Triple: [Liteyny Municipal Okrug, hasRegionalCode, RU-SPE]
Generated description
RU-SPE is the regional code for Saint Petersburg, a federal city and major cultural and economic center in northwestern Russia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: RU-SPE Target entity description: RU-SPE is the regional code for Saint Petersburg, a federal city and major cultural and economic center in northwestern Russia.
-
A.
Rus
Rus was a medieval East Slavic cultural and political realm that laid the foundations for the modern nations of Russia, Ukraine, and Belarus.
-
B.
RU
RU is the common abbreviation for Rutgers University, a major public research institution in New Jersey.
-
C.
RU
RU is the historic vehicle registration code that was used for the English county of Rutland.
-
D.
RU
RU is the two-letter ISO 3166 country code for the Russian Federation.
-
E.
RU
RU is the common abbreviation for Radboud University Nijmegen, a major research university located in Nijmegen, the Netherlands.
- F. None of above. chosen
Provenance (5 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_69ca839dbf608190a2f5990477115d29 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6783abe48190840e652fc2acf28f |
completed | April 1, 2026, 12:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc966f7d881908f4f80c2a0d820fe |
completed | April 3, 2026, 2:06 p.m. |
| NEDg | Description generation | batch_69cfc9ef5e548190a134c2bf0aa380b5 |
completed | April 3, 2026, 2:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cfca735c50819088c2805c62d96d62 |
completed | April 3, 2026, 2:10 p.m. |
Created at: March 30, 2026, 7:02 p.m.