Triple

T841919
Position Surface form Disambiguated ID Type / Status
Subject Mindanao E18195 entity
Predicate languageSpoken P151 FINISHED
Object Surigaonon 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: Surigaonon | Statement: [Mindanao, languageSpoken, Surigaonon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Surigaonon
Context triple: [Mindanao, languageSpoken, Surigaonon]
  • A. Tinogasta
    Tinogasta is a town in northwestern Argentina known for its wine production, hot springs, and location along the Andean mountain routes in Catamarca Province.
  • B. Ibanag
    Ibanag is an Austronesian language spoken primarily in the Cagayan Valley region of northern Luzon in the Philippines.
  • C. Batabanó
    Batabanó is a coastal municipality in western Cuba known for its fishing industry and ferry connections to nearby islands.
  • D. Calabarzon
    Calabarzon is a populous and industrialized region in the southern part of Luzon in the Philippines, known for its mix of urban centers, agricultural areas, and manufacturing hubs.
  • E. Ilonggo chosen
    Ilonggo is a major Austronesian language spoken primarily in Western Visayas and parts of Mindanao 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_69a49389f44881909a608fb27d89f247 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4abe6f0dc8190a1bebb5e21f4ceac completed March 1, 2026, 9:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69a826cf14808190a4afc2a7b6c66845 completed March 4, 2026, 12:34 p.m.
Created at: March 1, 2026, 7:38 p.m.