Triple

T15289429
Position Surface form Disambiguated ID Type / Status
Subject Mr. Right (2015 film) E365487 entity
Predicate filmingLocation P40 FINISHED
Object New Orleans E3902 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: New Orleans | Statement: [Mr. Right (2015 film), filmingLocation, New Orleans]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: New Orleans
Context triple: [Mr. Right (2015 film), filmingLocation, New Orleans]
  • A. New Orleans chosen
    New Orleans is a historic port city in southeastern Louisiana known for its vibrant jazz music, Creole cuisine, and distinctive French and Spanish-influenced architecture.
  • B. City of New Orleans
    City of New Orleans is a famous long-distance passenger train that runs between Chicago and New Orleans and was popularized by the folk song of the same name.
  • C. Nola
    Nola is an ancient town in southern Italy, historically significant in Roman times and known as the place where Emperor Augustus died.
  • D. Baton Rouge, Louisiana
    Baton Rouge, Louisiana is the capital city of Louisiana, known for its role as a political, industrial, and cultural center along the Mississippi River.
  • E. Shreveport
    Shreveport is a major city in northwestern Louisiana known for its role as a regional commercial, cultural, and transportation hub.
  • 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_69d85a103d9081908c1ea6c4c73ac8e3 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00e5635b4819092a69b5806d15bff completed April 15, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a009181980481908fb49e464aef8734 completed May 10, 2026, 2:09 p.m.
Created at: April 10, 2026, 3:15 a.m.