Triple

T22748061
Position Surface form Disambiguated ID Type / Status
Subject Municipality of Daanbantayan E562608 entity
Predicate hasBarangay P29835 FINISHED
Object Agujo 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: Agujo | Statement: [Municipality of Daanbantayan, hasBarangay, Agujo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Agujo
Context triple: [Municipality of Daanbantayan, hasBarangay, Agujo]
  • A. Agujo chosen
    Agujo is a coastal barangay in the municipality of Daanbantayan in Cebu, Philippines.
  • B. Anguinán
    Anguinán is a small rural settlement located in the Chilecito Department of La Rioja Province in northwestern Argentina.
  • C. Escuñau
    Escuñau is a small village in the municipality of Vielha e Mijaran in the Val d'Aran region of Catalonia, Spain.
  • D. Elciego
    Elciego is a small wine-producing town in Spain’s Rioja Alavesa region, known for its historic wineries and striking contemporary architecture.
  • E. Aguadas
    Aguadas is a historic Colombian town in the Caldas Department, known for its coffee culture, traditional hat-making, and well-preserved colonial architecture.
  • 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_69e245513a5c81908d5cb471b4fc429d completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f179b702388190b134dde5f80ea3cd completed April 29, 2026, 3:23 a.m.
Created at: April 17, 2026, 3:24 p.m.