Triple

T3228213
Position Surface form Disambiguated ID Type / Status
Subject Harper Beckham E67673 entity
Predicate hasFamilyResidence P22499 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: [Harper Beckham, hasFamilyResidence, Los Angeles]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Los Angeles
Context triple: [Harper Beckham, hasFamilyResidence, 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. 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.
  • E. Long Beach
    Long Beach is a coastal city in Southern California known for its busy port, waterfront attractions, and diverse urban community within the Los Angeles metropolitan area.
  • 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_69ad858c61888190a31196310d9b30b5 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaeb6f8588190a33a9d6c779e8992 completed March 8, 2026, 5:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2770201888190af6cbeded6d1ae56 completed March 12, 2026, 8:19 a.m.
Created at: March 8, 2026, 3:08 p.m.