Triple

T10116795
Position Surface form Disambiguated ID Type / Status
Subject PCH E223178 entity
Predicate passesThrough P225 FINISHED
Object Santa Monica E163687 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: Santa Monica | Statement: [PCH, passesThrough, Santa Monica]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Santa Monica
Context triple: [PCH, passesThrough, Santa Monica]
  • A. Santa Monica chosen
    Santa Monica is a coastal city in western Los Angeles County, California, known for its iconic pier, beaches, and vibrant tourism and entertainment scene.
  • B. Santa Monica
    Santa Monica is a coastal municipality on Siargao Island in the Philippines, known for its laid-back rural atmosphere, beaches, and fishing communities.
  • C. 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.
  • D. Long Beach
    Long Beach is a small coastal city in southern Mississippi known for its white-sand beaches, proximity to the Gulf of Mexico, and relaxed residential character.
  • E. Long Beach
    Long Beach is a popular, scenic stretch of sandy shoreline on Vietnam’s Phu Quoc Island, known for its sunsets, resorts, and calm tropical waters.
  • 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_69ca8422047c81909d66b717b8b18cf3 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cdd162fac0819084c74947c1f6688e completed April 2, 2026, 2:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69d32aa5032081909b2aab2f8eb4c4a7 completed April 6, 2026, 3:38 a.m.
Created at: March 30, 2026, 9:04 p.m.