Triple

T11964515
Position Surface form Disambiguated ID Type / Status
Subject Ilonggo people E284757 entity
Predicate alternateName P39 FINISHED
Object Ilonggos 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: Ilonggos | Statement: [Ilonggo people, alternateName, Ilonggos]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ilonggos
Context triple: [Ilonggo people, alternateName, Ilonggos]
  • A. Ilonggo chosen
    Ilonggo is a major Austronesian language spoken primarily in Western Visayas and parts of Mindanao in the Philippines.
  • B. Lagonglong
    Lagonglong is a coastal municipality in the province of Misamis Oriental on the island of Mindanao in the Philippines.
  • C. Kalamansig
    Kalamansig is a coastal municipality in the province of Sultan Kudarat in the Philippines, known for its fishing industry and diverse indigenous communities.
  • D. Mamanguape
    Mamanguape is a municipality in the Brazilian state of Paraíba, known for its historical colonial architecture and location near the Mamanguape River on the state’s northern coast.
  • E. Ibanag
    Ibanag is an Austronesian language spoken primarily in the Cagayan Valley region of northern Luzon in the Philippines.
  • 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_69d6ab2eaeb881909f7914758f859413 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903799f948190a5dc4d3822f3ff27 completed April 10, 2026, 2:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69f4594054f08190b28b35f62dfb9198 completed May 1, 2026, 7:41 a.m.
Created at: April 8, 2026, 9:45 p.m.