Triple

T4306172
Position Surface form Disambiguated ID Type / Status
Subject Burt Lancaster E99959 entity
Predicate placeOfDeath P21 FINISHED
Object Los Angeles E715 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: Los Angeles | Statement: [Burt Lancaster, placeOfDeath, Los Angeles]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Los Angeles
Context triple: [Burt Lancaster, placeOfDeath, Los Angeles]
  • A. Los Angeles chosen
    Los Angeles is a major U.S. metropolis known for its entertainment industry, cultural diversity, and sprawling urban landscape.
  • B. Los Ángeles
    Los Ángeles is a mid-sized Chilean city known as an important commercial and agricultural center in the south-central part of the country.
  • C. San Angeles
    San Angeles is a fictional futuristic megacity formed from the merger of Los Angeles and San Diego in the science fiction film "Demolition Man."
  • D. San Fransokyo
    San Fransokyo is a fictional futuristic hybrid city combining elements of San Francisco and Tokyo, serving as the primary setting of Disney's animated film "Big Hero 6."
  • E. Santa Monica
    Santa Monica is a coastal city in western Los Angeles County, California, known for its iconic pier, beaches, and vibrant tourism and entertainment scene.
  • 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_69b345528ebc8190b5abc7e95094792d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b350ba57ec8190afda7d8318e5d2d0 completed March 12, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5d055a1888190a188a8dba7d8af23 completed March 14, 2026, 9:17 p.m.
Created at: March 12, 2026, 11:09 p.m.