Triple

T25102479
Position Surface form Disambiguated ID Type / Status
Subject South Papua Province E628767 entity
Predicate linguaFranca P24056 FINISHED
Object Papuan Malay E155135 NE FINISHED

How this triple was built (1 step)

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: Papuan Malay | Statement: [South Papua Province, linguaFranca, Papuan Malay]

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_69e2ff3071548190b62d1ac237397197 completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f464be45448190b42c7d880d8550c8 completed May 1, 2026, 8:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a105cf104ac8190bc3c3be3076a5a7c completed May 22, 2026, 1:41 p.m.
Created at: April 18, 2026, 6:26 a.m.