Triple

T319051
Position Surface form Disambiguated ID Type / Status
Subject Gare Montparnasse E7771 entity
Predicate railService P522 FINISHED
Object TER
TER is a network of regional express trains in France that provides local passenger rail services across various regions.
 E41185 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 | Statement: [Gare Montparnasse, railService, TER]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TER
Context triple: [Gare Montparnasse, railService, TER]
  • A. TR
    TR is the two-letter ISO 3166-1 alpha-2 country code assigned to Turkey for international standardization and referencing.
  • B. TW
    TW is the two-letter ISO 3166 country code assigned to Taiwan (commonly referred to as Chinese Taipei in certain international contexts).
  • C. TH
    TH is the two-letter ISO 3166-1 alpha-2 country code assigned to Thailand for international standardization and identification.
  • D. the T
    The T is the public transit system serving the Greater Boston area, operated by the Massachusetts Bay Transportation Authority.
  • E. TC
    TC is the standard abbreviation for the IEEE Transactions on Computers, a leading peer-reviewed journal covering research in computer science and engineering.
  • 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
Triple: [Gare Montparnasse, railService, TER]
Generated description
TER is a network of regional express trains in France that provides local passenger rail services across various regions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TER
Target entity description: TER is a network of regional express trains in France that provides local passenger rail services across various regions.
  • A. TR
    TR is the two-letter ISO 3166-1 alpha-2 country code assigned to Turkey for international standardization and referencing.
  • B. TW
    TW is the two-letter ISO 3166 country code assigned to Taiwan (commonly referred to as Chinese Taipei in certain international contexts).
  • C. TH
    TH is the two-letter ISO 3166-1 alpha-2 country code assigned to Thailand for international standardization and identification.
  • D. the T
    The T is the public transit system serving the Greater Boston area, operated by the Massachusetts Bay Transportation Authority.
  • E. TC
    TC is the standard abbreviation for the IEEE Transactions on Computers, a leading peer-reviewed journal covering research in computer science and engineering.
  • 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_69a2e7e7af7881908890039d6be4e9b8 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ee016c408190beab4009653524db completed Feb. 28, 2026, 1:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3c8ba3304819099db7b60f2c83c8b completed March 1, 2026, 5:03 a.m.
NEDg Description generation batch_69a3c93ce2308190b2df5c939691a2ce completed March 1, 2026, 5:06 a.m.
NED2 Entity disambiguation (via description) batch_69a3ca020b7081909b54e311c173ef21 completed March 1, 2026, 5:09 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.