Triple

T7300530
Position Surface form Disambiguated ID Type / Status
Subject Boulogne-Billancourt E167837 entity
Predicate populationRankInHautsDeSeine P45844 FINISHED
Object largest commune by population LITERAL 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: largest commune by population | Statement: [Boulogne-Billancourt, populationRankInHautsDeSeine, largest commune by population]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: populationRankInHautsDeSeine
Context triple: [Boulogne-Billancourt, populationRankInHautsDeSeine, largest commune by population]
  • A. populationRankInFrance
    Indicates the relative position of an entity in an ordered list based on its population size within France.
  • B. economicRankInFrance
    Indicates the relative economic standing or ranking of an entity within the context of France’s economy.
  • C. hasPopulationRankInDepartment chosen
    Indicates the relative position of an entity’s population size compared to other entities within the same department.
  • D. hasArrondissement
    Indicates a relationship where an administrative unit or locality is associated with, or belongs to, a specific arrondissement.
  • E. locatedInMetropolitanFrance
    Indicates that the subject is geographically situated within the territory of metropolitan (continental) France.
  • F. None of above.

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_69c6888c820881909fc68f689fe1c251 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6ebaef4a081908fadc6d5e2621e80 completed March 27, 2026, 8:42 p.m.
PD Predicate disambiguation batch_69c6e76e67d88190bd3ca6864f45845a completed March 27, 2026, 8:24 p.m.
Created at: March 27, 2026, 3:01 p.m.