Triple

T1856443
Position Surface form Disambiguated ID Type / Status
Subject Bayonne E41714 entity
Predicate knownFor P22 FINISHED
Object Bayonne ham E206237 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: Bayonne ham | Statement: [Bayonne, knownFor, Bayonne ham]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bayonne ham
Context triple: [Bayonne, knownFor, Bayonne ham]
  • A. Bayonne ham chosen
    Bayonne ham is a traditional dry-cured French ham renowned for its delicate flavor and centuries-old artisanal production methods.
  • B. Bacon
    Bacon is a common English surname historically associated with notable figures such as the philosopher and statesman Francis Bacon.
  • C. Salo
    Salo is a town in southwestern Finland known for its electronics industry history and location along the Salo River.
  • D. Black Forest ham
    Black Forest ham is a traditional German dry-cured, smoked ham from the Black Forest region, prized for its dark exterior, distinctive smoky flavor, and protected regional designation.
  • E. Boudin
    Boudin is a French surname most famously associated with Eugène Boudin, a pioneering 19th-century landscape and marine painter linked to the origins of Impressionism.
  • 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_69a8864a83848190a4ec02721306c511 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb07e5ed48190a7b8858e2b355109 completed March 7, 2026, 4:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69add1cb5b708190a0b89b157ea9da58 completed March 8, 2026, 7:45 p.m.
Created at: March 4, 2026, 7:33 p.m.