Triple

T6483426
Position Surface form Disambiguated ID Type / Status
Subject Nîmes E146449 entity
Predicate locatedNear P294 FINISHED
Object Montpellier E178364 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: Montpellier | Statement: [Nîmes, locatedNear, Montpellier]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Montpellier
Context triple: [Nîmes, locatedNear, Montpellier]
  • A. Montpellier chosen
    Montpellier is a major city in southern France known for its medieval old town, vibrant university life, and proximity to the Mediterranean coast.
  • B. Toulouse
    Toulouse is a major city in southwestern France known for its aerospace industry, historic pink-brick architecture, and vibrant university and cultural life.
  • C. Rodez
    Rodez is a historic cathedral city in southern France that serves as the capital of the Aveyron department in the Occitanie region.
  • D. Béziers
    Béziers is a historic city in southern France known for its wine production, ancient Roman heritage, and the famous Feria de Béziers festival.
  • E. Albi
    Albi is a historic city in southern France renowned for its red-brick medieval architecture and the UNESCO-listed Episcopal City centered around Sainte-Cécile Cathedral.
  • 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_69c0090158c08190af0df9a2348d2d52 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c06a6cd0c4819085a921e6a361d91c completed March 22, 2026, 10:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8274d1994819089af156d634547ee completed March 28, 2026, 7:09 p.m.
Created at: March 22, 2026, 4:52 p.m.