Triple

T13796473
Position Surface form Disambiguated ID Type / Status
Subject Django (1966 film) E331529 entity
Predicate filmingLocation P40 FINISHED
Object Lazio, Italy E12614 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: Lazio, Italy | Statement: [Django (1966 film), filmingLocation, Lazio, Italy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lazio, Italy
Context triple: [Django (1966 film), filmingLocation, Lazio, Italy]
  • A. Lazio
    Lazio is a major professional football club based in Rome, Italy, known for competing in Serie A and for its passionate fan base and historic rivalry with AS Roma.
  • B. Lazio chosen
    Lazio is a central Italian region best known for encompassing the nation’s capital, Rome, and its rich historical and cultural heritage.
  • C. Cairano, Italy
    Cairano, Italy is a small hilltop village in the Campania region of southern Italy, known for its scenic landscapes and traditional rural character.
  • D. Arese, Italy
    Arese, Italy is a town in the Lombardy region best known for its historic Alfa Romeo automobile manufacturing plant and automotive heritage.
  • E. Montella, Italy
    Montella, Italy is a small town in the Campania region of southern Italy, known for its mountainous landscape and traditional chestnut production.
  • 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_69d81c58feb08190a77bca8bf7d6d20f completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de025be1f08190aac525d72d7dc0c3 completed April 14, 2026, 9:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7b08508688190b7e8c33e6b65e25d completed May 3, 2026, 8:31 p.m.
Created at: April 9, 2026, 10:11 p.m.