Triple

T2254521
Position Surface form Disambiguated ID Type / Status
Subject Limoges E49689 entity
Predicate railwayConnection P848 FINISHED
Object Bordeaux E6982 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: Bordeaux | Statement: [Limoges, railwayConnection, Bordeaux]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bordeaux
Context triple: [Limoges, railwayConnection, Bordeaux]
  • A. Bordeaux chosen
    Bordeaux is a renowned wine-producing region in southwestern France, famous for its prestigious red blends and long winemaking tradition.
  • B. Nantes
    Nantes is a historic port city in western France on the Loire River, known for its maritime heritage, cultural institutions, and vibrant arts scene.
  • C. Toulouse
    Toulouse is a major city in southwestern France known for its aerospace industry, historic pink-brick architecture, and vibrant university and cultural life.
  • D. Cahors
    Cahors is a historic town in southwestern France renowned for its medieval architecture, including the fortified Valentré Bridge, and its surrounding Malbec wine-producing vineyards.
  • E. Dijon
    Dijon is a historic city in eastern France renowned for its rich architectural heritage, former status as the capital of the Duchy of Burgundy, and its famous mustard.
  • 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_69a88aaa9250819095e127d0d77e8a32 completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc121af78819085b2e601d2f9bcdf completed March 7, 2026, 6:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69afbba881848190acaff6d216799c2d completed March 10, 2026, 6:35 a.m.
Created at: March 4, 2026, 7:47 p.m.