Triple

T17061327
Position Surface form Disambiguated ID Type / Status
Subject Pantabangan E413965 entity
Predicate subdivisionName P747 FINISHED
Object Pantabangan E413965 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: Pantabangan | Statement: [Pantabangan, subdivisionName, Pantabangan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pantabangan
Context triple: [Pantabangan, subdivisionName, Pantabangan]
  • A. Pantabangan chosen
    Pantabangan is a municipality in the Philippine province of Nueva Ecija known for the Pantabangan Dam and its role in irrigation and hydroelectric power generation.
  • B. Lubuagan
    Lubuagan is a landlocked, mountainous municipality in the Philippine province of Kalinga known for its rich indigenous culture and history.
  • C. Kapangan
    Kapangan is a rural municipality in the mountainous province of Benguet in the Philippines, known for its cool climate, highland farms, and scenic Cordillera landscapes.
  • D. Pangwali
    Pangwali is an Indo-Aryan language variety spoken primarily in the Pangi Valley of Himachal Pradesh, India, and is considered a dialect of the Western Pahari group.
  • E. Sarangani
    Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
  • 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_69d886cde3d481908d4d01ba88ba7eb7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3db7dea7481909e3e0bc836d27336 completed April 18, 2026, 7:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01234be4e4819084701902e7bd3a27 completed May 11, 2026, 12:31 a.m.
Created at: April 10, 2026, 5:34 a.m.