Triple

T12972088
Position Surface form Disambiguated ID Type / Status
Subject Avenida Ashford E321423 entity
Predicate locatedIn P40 FINISHED
Object San Juan E274337 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: San Juan | Statement: [Avenida Ashford, locatedIn, San Juan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Juan
Context triple: [Avenida Ashford, locatedIn, San Juan]
  • A. San Juan
    San Juan is an Argentine wine-producing region recognized for its significant Malbec production.
  • B. San Juan
    San Juan is a suburban town in Trinidad and Tobago located just east of the capital, Port of Spain, known for its bustling commercial activity and residential communities.
  • C. San Juan chosen
    San Juan is a highly urbanized city in Metro Manila, Philippines, known for its historical sites, dense residential and commercial areas, and role in the capital region’s urban core.
  • D. San Juan
    San Juan is a coastal municipality on Siquijor Island in the Philippines known for its beaches, dive spots, and laid-back tourist resorts.
  • E. San Juan
    San Juan is a coastal municipality in the Philippine province of Batangas known for its beaches, dive spots, and heritage sites.
  • 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_69d80763bd6c819094437da5b20b01d2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e418d548190be1c73db76cb3aa8 completed April 10, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6b8ea91f08190b1daf6d05621acf9 completed May 3, 2026, 2:54 a.m.
Created at: April 9, 2026, 8:36 p.m.