Triple

T3391176
Position Surface form Disambiguated ID Type / Status
Subject The Point at El Segundo E71420 entity
Predicate city P40 FINISHED
Object El Segundo E52470 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: El Segundo | Statement: [The Point at El Segundo, city, El Segundo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: El Segundo
Context triple: [The Point at El Segundo, city, El Segundo]
  • A. El Segundo, California chosen
    El Segundo, California is a coastal city in Los Angeles County known for its concentration of aerospace, defense, and technology companies.
  • B. Toa Baja
    Toa Baja is a coastal municipality in northern Puerto Rico, known for its proximity to San Juan and its mix of urban, industrial, and residential areas.
  • C. Doral
    Doral is a discount cigarette brand produced by R.J. Reynolds Tobacco Company, known for its value-oriented positioning in the U.S. tobacco market.
  • D. Rosarito
    Rosarito is a coastal resort city in northern Baja California, Mexico, known for its beaches, tourism, and proximity to the U.S. border.
  • E. Nueva Valencia
    Nueva Valencia is a coastal municipality on the island province of Guimaras in the Philippines, known for its beaches and island-hopping destinations.
  • 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_69ad85a9c4a88190a854019341cb3b60 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb6682c708190b76a7a16cee7c5aa completed March 8, 2026, 5:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3345bfbc88190b1189bef9f2cd73f completed March 12, 2026, 9:47 p.m.
Created at: March 8, 2026, 3:14 p.m.