Triple

T3624297
Position Surface form Disambiguated ID Type / Status
Subject Pierre Puget E76798 entity
Predicate notableWorkLocation P3858 FINISHED
Object Toulon E83956 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: Toulon | Statement: [Pierre Puget, notableWorkLocation, Toulon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Toulon
Context triple: [Pierre Puget, notableWorkLocation, Toulon]
  • A. Toulon chosen
    Toulon is a major port city on France’s Mediterranean coast that serves as the principal base of the French Navy.
  • B. La Rochelle
    La Rochelle is a historic French Atlantic port city that became a major stronghold and refuge for Huguenots during the French Wars of Religion.
  • 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. Marseille
    Marseille is a historic Mediterranean port city in southern France known for its diverse culture, maritime heritage, and role as a major economic hub.
  • E. 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.
  • 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_69ad85dc03948190b35b7189e4175bcc completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc2d9845c8190ad65b2471000dfa0 completed March 8, 2026, 6:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69be5c69987881908dc2b6286fec73c2 completed March 21, 2026, 8:52 a.m.
Created at: March 8, 2026, 3:23 p.m.