Triple

T33120895
Position Surface form Disambiguated ID Type / Status
Subject M4 motorway E847591 entity
Predicate hasServiceArea P82 FINISHED
Object Reading services E22663 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: Reading services | Statement: [M4 motorway, hasServiceArea, Reading services]

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_69f3495751a081909850af5843da40dc completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d718e52c81908e158907db3ec765 completed May 3, 2026, 5:03 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34f03c0b9c81909420d6f21becc9f0 completed June 19, 2026, 7:31 a.m.
Created at: May 1, 2026, 1:27 a.m.