Triple

T5791195
Position Surface form Disambiguated ID Type / Status
Subject Count of Vermandois E128396 entity
Predicate associatedWithCity P1481 FINISHED
Object Saint-Quentin E214011 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: Saint-Quentin | Statement: [Count of Vermandois, associatedWithCity, Saint-Quentin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saint-Quentin
Context triple: [Count of Vermandois, associatedWithCity, Saint-Quentin]
  • A. Saint-Quentin chosen
    Saint-Quentin is a historic town in northern France known for its Gothic basilica, Art Deco architecture, and role as a regional administrative and commercial center.
  • B. Mézières
    Mézières is a French town historically known as a military and engineering education center, notably associated with the prestigious École royale du génie.
  • C. Soissons
    Soissons is a historic town in northern France known for its strategic military importance and notable battles throughout European history.
  • D. Château-Thierry
    Château-Thierry is a historic town in northern France known for its World War I battlefields and its association with the poet Jean de La Fontaine.
  • E. Péronne
    Péronne is a historic town in northern France known for its role in World War I and its location in the Somme department.
  • 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_69c00845ca68819081a2ce3ecca577f7 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02a56c73c81908a1c72c86e474b54 completed March 22, 2026, 5:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0b0d71b7881909108c7347ce91317 completed March 23, 2026, 3:17 a.m.
Created at: March 22, 2026, 3:51 p.m.