Triple

T11840550
Position Surface form Disambiguated ID Type / Status
Subject Urban Hiligaynon E281638 entity
Predicate hasBaseLanguage P19951 FINISHED
Object Ilonggo E58819 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: Ilonggo | Statement: [Urban Hiligaynon, hasBaseLanguage, Ilonggo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ilonggo
Context triple: [Urban Hiligaynon, hasBaseLanguage, Ilonggo]
  • A. Ilonggo chosen
    Ilonggo is a major Austronesian language spoken primarily in Western Visayas and parts of Mindanao in the Philippines.
  • B. Itang
    Itang is a town in western Ethiopia that serves as one of the principal urban centers of the Gambela Region.
  • C. Pilcaniyeu
    Pilcaniyeu is a small town in Argentina’s Patagonia region, located in the Andean area of Río Negro Province and known for its rural character and nearby natural landscapes.
  • D. Kalamansig
    Kalamansig is a coastal municipality in the province of Sultan Kudarat in the Philippines, known for its fishing industry and diverse indigenous communities.
  • 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_69d6ab276f8c8190b1966a0ef11349ac completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8be15fb2481908f514781ce2c617f completed April 10, 2026, 9:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69f1678668ac81909bddf67e8c176757 completed April 29, 2026, 2:05 a.m.
Created at: April 8, 2026, 9:43 p.m.