Triple

T38571116

Position	Surface form	Disambiguated ID	Type / Status
Subject	Kimbo Slice	`E929273`	entity
Predicate	foughtAgainst	`P12`	FINISHED
Object	Dada 5000 Dada 5000 is an American bare-knuckle boxer and mixed martial artist best known for his backyard fighting roots and his high-profile, controversial bout with Kimbo Slice in Bellator MMA.	`E2274853`	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: Dada 5000 | Statement: [Kimbo Slice, foughtAgainst, Dada 5000]

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: Dada 5000
Triple: [Kimbo Slice, foughtAgainst, Dada 5000]

Generated description

Dada 5000 is an American bare-knuckle boxer and mixed martial artist best known for his backyard fighting roots and his high-profile, controversial bout with Kimbo Slice in Bellator MMA.

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_69f76ebd2248819083978362d81fa35e`	completed	May 3, 2026, 3:50 p.m.
NER	Named-entity recognition	`batch_69fcd90e2a288190bee9bbae8c27a521`	completed	May 7, 2026, 6:25 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a41e04b81b88190a5e274527e1e3704`	completed	June 29, 2026, 3:02 a.m.
NEDg	Description generation	`batch_6a41e12f868c8190917fde5775e28d19`	completed	June 29, 2026, 3:06 a.m.
NED2	Entity disambiguation (via description)	`batch_6a41e1c14b4c81908b2d6358dbd3ae0f`	completed	June 29, 2026, 3:08 a.m.

Created at: May 3, 2026, 4:32 p.m.