Triple
T8719827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Transport Express Régional |
E206983
|
entity |
| Predicate | rebrandedAs |
P65
|
FINISHED |
| Object |
TER (brand)
TER is a French regional rail service brand operated by SNCF, providing local passenger train connections across various regions of France.
|
E754104
|
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: TER (brand) | Statement: [Transport Express Régional, rebrandedAs, TER (brand)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TER (brand) Context triple: [Transport Express Régional, rebrandedAs, TER (brand)]
-
A.
Tec Toy
Tec Toy is a Brazilian electronics and video game company best known for localizing, manufacturing, and popularizing Sega consoles and games in Brazil.
-
B.
Ter
The Ter is a river in northeastern Catalonia, Spain, that flows through cities such as Girona before emptying into the Mediterranean Sea.
-
C.
TRE
TRE is the station code for the Trenton Transit Center, a major rail hub in Trenton, New Jersey serving Amtrak, NJ Transit, and SEPTA trains.
-
D.
TRE
TRE is the IATA airport code for Tiree Airport, which serves the island of Tiree in Scotland’s Inner Hebrides.
-
E.
T-Engineering
T-Engineering is an engineering firm known for its role in designing major infrastructure projects, including the Yavuz Sultan Selim Bridge in Turkey.
- 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: TER (brand) Triple: [Transport Express Régional, rebrandedAs, TER (brand)]
Generated description
TER is a French regional rail service brand operated by SNCF, providing local passenger train connections across various regions of France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: TER (brand) Target entity description: TER is a French regional rail service brand operated by SNCF, providing local passenger train connections across various regions of France.
-
A.
Tec Toy
Tec Toy is a Brazilian electronics and video game company best known for localizing, manufacturing, and popularizing Sega consoles and games in Brazil.
-
B.
Ter
The Ter is a river in northeastern Catalonia, Spain, that flows through cities such as Girona before emptying into the Mediterranean Sea.
-
C.
TRE
TRE is the station code for the Trenton Transit Center, a major rail hub in Trenton, New Jersey serving Amtrak, NJ Transit, and SEPTA trains.
-
D.
TRE
TRE is the IATA airport code for Tiree Airport, which serves the island of Tiree in Scotland’s Inner Hebrides.
-
E.
T-Engineering
T-Engineering is an engineering firm known for its role in designing major infrastructure projects, including the Yavuz Sultan Selim Bridge in Turkey.
- 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_69ca835811d8819081ea00fd2a2c9a1c |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d02a52c81909f93622ae6920b80 |
completed | March 31, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf28f599a481908e93bc5b5c41296e |
completed | April 3, 2026, 2:41 a.m. |
| NEDg | Description generation | batch_69cf2bd222b08190907ba7e98991996e |
completed | April 3, 2026, 2:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf2fcb5e7c819086b441d1ef4fc368 |
completed | April 3, 2026, 3:11 a.m. |
Created at: March 30, 2026, 6:36 p.m.