Triple

T15339255
Position Surface form Disambiguated ID Type / Status
Subject Rouen metropolitan area E366746 entity
Predicate hasPart P35 FINISHED
Object Petit-Couronne E1022224 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: Petit-Couronne | Statement: [Rouen metropolitan area, hasPart, Petit-Couronne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Petit-Couronne
Context triple: [Rouen metropolitan area, hasPart, Petit-Couronne]
  • A. Petit-Couronne chosen
    Petit-Couronne is a commune in the Seine-Maritime department of northern France, located near Rouen in the Normandy region.
  • B. Remigny
    Remigny is a small wine-producing village in the Burgundy region of eastern France, situated near the renowned appellation of Santenay.
  • C. Pommereuil
    Pommereuil is a small commune in the Nord department of northern France.
  • D. Condécourt
    Condécourt is a small commune in the Val-d'Oise department in the Île-de-France region of northern France.
  • E. Ermontoise
    Ermontoise is the French demonym referring to a female inhabitant or native of the town of Ermont in France.
  • 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_69d85a1355608190a6673ddb67231d54 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e12eb7c8190944a260aa1aa9156 completed April 16, 2026, 1:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff01f2ee9c819080fce24ed13a07c7 completed May 9, 2026, 9:44 a.m.
Created at: April 10, 2026, 3:17 a.m.