Triple

T37103577
Position Surface form Disambiguated ID Type / Status
Subject Lüshun Port E918775 entity
Predicate hasFormerName P65 FINISHED
Object Ryojun E926912 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: Ryojun | Statement: [Lüshun Port, hasFormerName, Ryojun]

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_69f76e9b99c8819096164b21ff5bd996 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb2ff117cc8190af92c21db441a854 completed May 6, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a5a8de38ae0819087145d517d3484ef completed July 17, 2026, 8:17 p.m.
Created at: May 3, 2026, 4:14 p.m.