Triple

T35414638

Position	Surface form	Disambiguated ID	Type / Status
Subject	Steve Reed	`E1023607`	entity
Predicate	birthName	`P65`	FINISHED
Object	Steven Mark Reed Steven Mark Reed is a British Labour politician who has served as Member of Parliament for Croydon North since 2012 and held various shadow ministerial roles.	`E2139086`	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: Steven Mark Reed | Statement: [Steve Reed, birthName, Steven Mark Reed]

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: Steven Mark Reed
Triple: [Steve Reed, birthName, Steven Mark Reed]

Generated description

Steven Mark Reed is a British Labour politician who has served as Member of Parliament for Croydon North since 2012 and held various shadow ministerial roles.

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_69f76df54bac8190bd0d3b0eb35cda5f`	completed	May 3, 2026, 3:47 p.m.
NER	Named-entity recognition	`batch_69f7956b1d80819081655f683ce78d5b`	completed	May 3, 2026, 6:35 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a382cd4a1f081909c0172acb5b26a75`	completed	June 21, 2026, 6:26 p.m.
NEDg	Description generation	`batch_6a382d5989bc8190965463f119c6679e`	completed	June 21, 2026, 6:28 p.m.
NED2	Entity disambiguation (via description)	`batch_6a382e1f37188190ac188d12cc6dce07`	completed	June 21, 2026, 6:31 p.m.

Created at: May 3, 2026, 4:03 p.m.