Triple

T13774695
Position Surface form Disambiguated ID Type / Status
Subject Oras E330973 entity
Predicate locatedEastOf P4240 FINISHED
Object Borongan E327619 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: Borongan | Statement: [Oras, locatedEastOf, Borongan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Borongan
Context triple: [Oras, locatedEastOf, Borongan]
  • A. Borongan chosen
    Borongan is a coastal city in Eastern Samar, Philippines, known as a regional center and gateway to the natural attractions of Samar Island.
  • B. Bungsuan
    Bungsuan is a barangay (village-level administrative division) of the municipality of Dumalag in the province of Capiz, Philippines.
  • C. Bangu
    Bangu is a working-class neighborhood in the West Zone of Rio de Janeiro, Brazil, known for its hot climate, historic textile industry, and the Bangu Atlético Clube football team.
  • D. Bantay
    Bantay is a historic municipality in Ilocos Sur, Philippines, known for its centuries-old church and iconic bell tower overlooking the town.
  • E. Babatngon
    Babatngon is a coastal municipality in the province of Leyte in the Philippines, known for its fishing industry and rural communities.
  • 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_69d81c583b0081909e408a17db517a21 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de023774b48190b19e43e87b94ba77 completed April 14, 2026, 9 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7b07460c081908b3836a3ec382961 completed May 3, 2026, 8:30 p.m.
Created at: April 9, 2026, 10:10 p.m.