Triple

T8788441
Position Surface form Disambiguated ID Type / Status
Subject TER Nouvelle-Aquitaine E209099 entity
Predicate servesCity P82 FINISHED
Object Tarbes E109577 NE FINISHED

How this triple was built (2 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: Tarbes | Statement: [TER Nouvelle-Aquitaine, servesCity, Tarbes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tarbes
Context triple: [TER Nouvelle-Aquitaine, servesCity, Tarbes]
  • A. Tarbes chosen
    Tarbes is a historic city in southwestern France, serving as the capital of the Hautes-Pyrénées department at the foot of the Pyrenees.
  • B. Bagnères-de-Bigorre
    Bagnères-de-Bigorre is a spa and ski resort town in the French Pyrenees, known for its thermal baths and mountain tourism.
  • C. Narbonne
    Narbonne is a historic city in southern France known for its Roman heritage, medieval architecture, and former status as an important Mediterranean port.
  • D. Saint-Girons
    Saint-Girons is a small town in the Ariège department of southwestern France, situated in the foothills of the Pyrenees.
  • E. Colomiers
    Colomiers is a suburban city in southwestern France, known as part of the Toulouse metropolitan area and for its strong aerospace and industrial sectors.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca836168108190bb43d3dc235c1f55 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5f89a84c819085d4cfe4e6dfbda8 completed March 31, 2026, 11:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf5210454c8190aa83d941893a4bc5 completed April 3, 2026, 5:37 a.m.
Created at: March 30, 2026, 6:43 p.m.