Triple

T36231972

Position	Surface form	Disambiguated ID	Type / Status
Subject	Wonder Man	`E891268`	entity
Predicate	featuresCharacter	`P626`	FINISHED
Object	Buzzy Bellew Buzzy Bellew is a murdered nightclub singer whose ghost helps his timid cousin become a confident hero in the 1945 fantasy-comedy film "Wonder Man."	`E2175554`	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: Buzzy Bellew | Statement: [Wonder Man, featuresCharacter, Buzzy Bellew]

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: Buzzy Bellew
Triple: [Wonder Man, featuresCharacter, Buzzy Bellew]

Generated description

Buzzy Bellew is a murdered nightclub singer whose ghost helps his timid cousin become a confident hero in the 1945 fantasy-comedy film "Wonder Man."

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_69f76e4387048190a1b27bcbf4ec7423`	completed	May 3, 2026, 3:48 p.m.
NER	Named-entity recognition	`batch_69f7b5a3b0ac8190aa83dba27a58964f`	completed	May 3, 2026, 8:52 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a394d3c0494819085069b3bfd9e3429`	completed	June 22, 2026, 2:57 p.m.
NEDg	Description generation	`batch_6a394dcb8f0481908f9709a8c906994a`	completed	June 22, 2026, 2:59 p.m.
NED2	Entity disambiguation (via description)	`batch_6a395093c23081909967471f090c9410`	completed	June 22, 2026, 3:11 p.m.

Created at: May 3, 2026, 4:09 p.m.