Triple

T37400004

Position	Surface form	Disambiguated ID	Type / Status
Subject	Bristol Beverley	`E928966`	entity
Predicate	operatedAt	`P837`	FINISHED
Object	RAF Dishforth RAF Dishforth is a former Royal Air Force station in North Yorkshire, England, historically used as a transport airfield and later by Army Air Corps helicopter units.	`E2242895`	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: RAF Dishforth | Statement: [Bristol Beverley, operatedAt, RAF Dishforth]

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: RAF Dishforth
Triple: [Bristol Beverley, operatedAt, RAF Dishforth]

Generated description

RAF Dishforth is a former Royal Air Force station in North Yorkshire, England, historically used as a transport airfield and later by Army Air Corps helicopter units.

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_69f76ebbf79c8190b85bbcf3a6be57e4`	completed	May 3, 2026, 3:50 p.m.
NER	Named-entity recognition	`batch_69fb8d5c71ec8190b908ea6e50a6f951`	completed	May 6, 2026, 6:50 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a40f165a28881909262db8241bb7032`	completed	June 28, 2026, 10:03 a.m.
NEDg	Description generation	`batch_6a40f22972e48190a673737cf741e5aa`	completed	June 28, 2026, 10:06 a.m.
NED2	Entity disambiguation (via description)	`batch_6a40f2aa79808190a3952e3cade199a1`	completed	June 28, 2026, 10:08 a.m.

Created at: May 3, 2026, 4:16 p.m.