Triple

T23061454
Position Surface form Disambiguated ID Type / Status
Subject Tau-sa Laya dialect E574312 entity
Predicate hasAlternativeName P39 FINISHED
Object Taw sa Laya 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: Taw sa Laya | Statement: [Tau-sa Laya dialect, hasAlternativeName, Taw sa Laya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taw sa Laya
Context triple: [Tau-sa Laya dialect, hasAlternativeName, Taw sa Laya]
  • A. Tau sa Laya chosen
    Tau sa Laya is a dialect of the Sama-Bajau language cluster spoken by coastal communities in the southern Philippines.
  • B. Talitay
    Talitay is a municipality located in the province of Maguindanao in the Bangsamoro Autonomous Region in Muslim Mindanao, Philippines.
  • C. Taif
    Taif is a city in western Saudi Arabia known for its cool climate, rose cultivation, and historical significance as a summer resort and cultural center.
  • D. Talaytay
    Talaytay is a barangay (village-level administrative division) of the municipality of Argao in the province of Cebu, Philippines.
  • E. Tajuan
    Tajuan is the given first name of former NFL cornerback Ty Law.
  • 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_69e245ba7ae48190be606dbc54120e39 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1899ff96081908d89a07a3b1065c8 completed April 29, 2026, 4:31 a.m.
Created at: April 17, 2026, 3:55 p.m.