Triple

T2521094
Position Surface form Disambiguated ID Type / Status
Subject The Real World E55523 entity
Predicate subsequentSeasonLocationsInclude P39593 FINISHED
Object Paris E568 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: Paris | Statement: [The Real World, subsequentSeasonLocationsInclude, Paris]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paris
Context triple: [The Real World, subsequentSeasonLocationsInclude, Paris]
  • A. Paris chosen
    Paris is the capital and largest city of France, renowned for its historic architecture, art, fashion, and cultural influence worldwide.
  • B. Paris
    Paris is a prince of Troy in Greek mythology, best known for judging the beauty contest of the goddesses and for abducting Helen, which sparked the Trojan War.
  • C. Lyon
    Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
  • D. Palaiseau
    Palaiseau is a suburban commune in the southern outskirts of Paris, France, known for hosting major scientific and engineering institutions.
  • E. Boulogne-Billancourt
    Boulogne-Billancourt is a densely populated suburban city just southwest of central Paris, known as a major economic and media hub in the Île-de-France region.
  • 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_69ab49e4749c8190813311efd1630f1b completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd83043708190929e033f6a6166a4 completed March 7, 2026, 7:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69af5cce716c8190b87b117b270b9a84 completed March 9, 2026, 11:50 p.m.
Created at: March 6, 2026, 9:46 p.m.