Triple

T57258
Position Surface form Disambiguated ID Type / Status
Subject Oppenheimer (2023 film) E1131 entity
Predicate setInPlace P1957 FINISHED
Object New Mexico E34095 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 Mexico | Statement: [Oppenheimer (2023 film), setInPlace, New Mexico]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: New Mexico
Context triple: [Oppenheimer (2023 film), setInPlace, New Mexico]
  • A. New Mexico chosen
    New Mexico is a southwestern U.S. state known for its diverse landscapes, rich Native American and Hispanic cultural heritage, and historic cities like Santa Fe and Albuquerque.
  • B. Arizona
    Arizona is a southwestern U.S. state known for its desert climate, the Grand Canyon, and major cities like Phoenix and Tucson.
  • C. Coahuila y Tejas
    Coahuila y Tejas was a Mexican state in the early 19th century that combined the regions of Coahuila and Texas before Texas’s independence and the formation of the Republic of Texas.
  • D. Nevada
    Nevada is a western U.S. state known for its vast deserts, legalized gambling, and the entertainment hub of Las Vegas.
  • E. Texas
    Texas is the second-largest U.S. state by both area and population, known for its diverse landscapes, major cities like Houston and Dallas, and significant cultural and economic influence.
  • 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_69a248adc5b48190aa8db9fb092fb28a completed Feb. 28, 2026, 1:45 a.m.
NER Named-entity recognition batch_69a24ec4d84c81908d85a1e941dbcd19 completed Feb. 28, 2026, 2:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3c40fb3c88190818cc5c2560b1253 completed March 1, 2026, 4:43 a.m.
Created at: Feb. 28, 2026, 1:50 a.m.