Triple

T36402664

Position	Surface form	Disambiguated ID	Type / Status
Subject	M1 motorway Junction 24	`E896670`	entity
Predicate	hasNearbyJunction	`P31669`	FINISHED
Object	M1 motorway Junction 25 M1 motorway Junction 25 is a major interchange on England’s M1 near Nottingham, providing access to the A52 and serving as a key route into the East Midlands.	`E2183273`	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: M1 motorway Junction 25 | Statement: [M1 motorway Junction 24, hasNearbyJunction, M1 motorway Junction 25]

NEDg Description generation gpt-5.1

Instruction

Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"

Input

Entity: M1 motorway Junction 25
Triple: [M1 motorway Junction 24, hasNearbyJunction, M1 motorway Junction 25]

Generated description

M1 motorway Junction 25 is a major interchange on England’s M1 near Nottingham, providing access to the A52 and serving as a key route into the East Midlands.

Provenance (5 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_69f76e53b81081908d3b81860593f38a`	completed	May 3, 2026, 3:48 p.m.
NER	Named-entity recognition	`batch_69f7bd16c1a881909acf1d69357eb8ed`	completed	May 3, 2026, 9:24 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a39c401797c8190a14837b5461187f0`	completed	June 22, 2026, 11:23 p.m.
NEDg	Description generation	`batch_6a39c4b2bf5c819082087f442c325927`	completed	June 22, 2026, 11:26 p.m.
NED2	Entity disambiguation (via description)	`batch_6a39c5c24c148190b5cce41203d557a1`	completed	June 22, 2026, 11:31 p.m.

Created at: May 3, 2026, 4:10 p.m.