Triple

T7901158
Position Surface form Disambiguated ID Type / Status
Subject Central Sama E183454 entity
Predicate hasDialect P4251 FINISHED
Object Siasi Sama E700459 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: Siasi Sama | Statement: [Central Sama, hasDialect, Siasi Sama]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siasi Sama
Context triple: [Central Sama, hasDialect, Siasi Sama]
  • A. Sama
    Sama is an Austronesian language spoken by the Sama-Bajau people of the southern Philippines and parts of Malaysia and Indonesia.
  • B. Bongao Sama chosen
    Bongao Sama is a dialect of the Central Sama language spoken primarily by the Sama people in and around Bongao in the southern Philippines.
  • C. Siasi
    Siasi is an island municipality in the southern Philippines known for its predominantly Muslim population, fishing-based economy, and location within the Sulu Sea.
  • D. Siya
    Siya is an alternate name for the Avatime language, a Kwa language spoken in the Volta Region of Ghana.
  • E. Dupax Isinay
    Dupax Isinay is a regional dialect of the Isinay language traditionally spoken in the Dupax area of Nueva Vizcaya in the northern 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_69ca828d13088190b222be7aa9f9315c completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3a3f4c2c81909ae70b0acf4729be completed March 31, 2026, 3:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbdfd2dbbc8190b7b1e45b7f0b7515 completed March 31, 2026, 2:53 p.m.
Created at: March 30, 2026, 5:02 p.m.