Triple

T14456025
Position Surface form Disambiguated ID Type / Status
Subject He Loves Me... He Loves Me Not E358460 entity
Predicate filmingLocation P40 FINISHED
Object France E861 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: France | Statement: [He Loves Me... He Loves Me Not, filmingLocation, France]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: France
Context triple: [He Loves Me... He Loves Me Not, filmingLocation, France]
  • A. France chosen
    France is a major Western European nation known for its influential history, culture, and economy, and as a founding member of the European Union and the United Nations.
  • B. Francia
    Francia is the surname of American rower and two-time Olympic gold medalist Susan Francia.
  • C. Pays Royannais
    Pays Royannais is a coastal area in southwestern France centered around the town of Royan, known for its seaside resorts and Atlantic beaches.
  • D. de France
    "de France" is a dynastic surname historically used by members of the French royal family, particularly the legitimate children of reigning kings of France.
  • E. Francie
    Francie is a diminutive given name, typically used as a nickname for Francis or Frances.
  • 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_69d82794dfa081909b9134ad2e32244b completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de91a9c0d48190ae015e5e0db806ca completed April 14, 2026, 7:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd64898c088190ab4eef32ca4f5ed6 completed May 8, 2026, 4:20 a.m.
Created at: April 10, 2026, 1:19 a.m.