Triple

T12812021
Position Surface form Disambiguated ID Type / Status
Subject Stamford E306293 entity
Predicate hasTwinTown P919 FINISHED
Object Givry E289276 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: Givry | Statement: [Stamford, hasTwinTown, Givry]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Givry
Context triple: [Stamford, hasTwinTown, Givry]
  • A. Givry chosen
    Givry is a Burgundy wine appellation in eastern France, noted for its predominantly Pinot Noir red wines with a reputation for good value and quality.
  • B. Gagny
    Gagny is a suburban commune in the eastern outskirts of Paris, France, known primarily as a residential town within the Seine-Saint-Denis department.
  • C. Chauvigny
    Chauvigny is a historic town in western France known for its medieval fortifications and picturesque setting in the Vienne department of the Nouvelle-Aquitaine region.
  • D. Brière
    Brière is a French-language surname most prominently associated with former NHL player and current hockey executive Daniel Brière.
  • E. Ferrière
    Ferrière is a French-language surname of Swiss origin borne by various notable individuals, including social worker and humanitarian Suzanne Ferrière.
  • 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_69d7bdf46c448190b1faa55aaacb6317 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96e9adcf08190a12801adcc613477 completed April 10, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75467a1a081908395b48615e3ea9b completed May 3, 2026, 1:57 p.m.
Created at: April 9, 2026, 5:31 p.m.