Triple

T18891541
Position Surface form Disambiguated ID Type / Status
Subject Glan E462101 entity
Predicate locatedIn P40 FINISHED
Object Sarangani NE NERFINISHED

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: Sarangani | Statement: [Glan, locatedIn, Sarangani]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sarangani
Context triple: [Glan, locatedIn, Sarangani]
  • A. Sarangani chosen
    Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
  • B. Danao
    Danao is a coastal city and municipality on Cebu Island in the Philippines known for its historical significance and local industries.
  • C. Marawila
    Marawila is a coastal town in Sri Lanka known for its beaches, fishing community, and tourism-oriented resorts.
  • D. Karagawan
    Karagawan is a regional dialect of the Isnag language spoken by the Isnag people of northern Luzon in the Philippines.
  • E. Maragondon
    Maragondon is a historic rural municipality in the province of Cavite in the Philippines, known for its Spanish-era heritage sites and nearby natural attractions.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8dcfc3430819095ee6fc0eb4c06a5 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5c47ab34c8190bfb69444a5263538 completed April 20, 2026, 6:15 a.m.
Created at: April 10, 2026, 11:58 a.m.