Triple

T16350454
Position Surface form Disambiguated ID Type / Status
Subject Palembang LRT E397047 entity
Predicate crossesRiver P416 FINISHED
Object Musi River E167825 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: Musi River | Statement: [Palembang LRT, crossesRiver, Musi River]

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_69d87f26864c819088365ca381a003c2 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2facb37d0819093fe45446f1e79c1 completed April 18, 2026, 3:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a24ec0f80a88190aa6f4c69962d7359 completed June 7, 2026, 3:57 a.m.
Created at: April 10, 2026, 5:07 a.m.