Triple

T8161945
Position Surface form Disambiguated ID Type / Status
Subject Pateros E190595 entity
Predicate locatedNear P294 FINISHED
Object Taguig E104725 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: Taguig | Statement: [Pateros, locatedNear, Taguig]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taguig
Context triple: [Pateros, locatedNear, Taguig]
  • A. Taguig chosen
    Taguig is a highly urbanized city in Metro Manila in the Philippines, known for the Bonifacio Global City (BGC) business district and rapid commercial and residential development.
  • B. Kawit
    Kawit is a historic coastal municipality in the Philippine province of Cavite, best known as the site where Philippine independence from Spain was first proclaimed in 1898.
  • C. Malabon
    Malabon is a coastal city in the northern part of Metro Manila in the Philippines, known for its historic districts, flood-prone waterways, and distinctive local cuisine.
  • D. Muntinlupa
    Muntinlupa is a highly urbanized city in the southern part of Metro Manila in the Philippines, known for housing the New Bilibid Prison and major commercial and residential developments like Alabang.
  • E. Mandaluyong
    Mandaluyong is a highly urbanized city in the Philippines known as part of Metro Manila’s central business and commercial district.
  • 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_69ca82c0ef14819083713f4473dd847c completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb4556b45c819089eb15ad027b036a completed March 31, 2026, 3:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce88498cf081909c25c292c014fe8c completed April 2, 2026, 3:16 p.m.
Created at: March 30, 2026, 5:38 p.m.