Triple

T35886039
Position Surface form Disambiguated ID Type / Status
Subject UFC 99 E1037644 entity
Predicate fighterOnCard P109204 FINISHED
Object Mike Swick
Mike Swick is an American mixed martial artist best known for his tenure as a welterweight and middleweight contender in the UFC and as a cast member on the first season of The Ultimate Fighter.
E2160461 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: Mike Swick | Statement: [UFC 99, fighterOnCard, Mike Swick]
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: Mike Swick
Triple: [UFC 99, fighterOnCard, Mike Swick]
Generated description
Mike Swick is an American mixed martial artist best known for his tenure as a welterweight and middleweight contender in the UFC and as a cast member on the first season of The Ultimate Fighter.

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_69f76e1f4d748190bb55594d8441d70e completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7aa09411481909b2130c4c2b137f5 completed May 3, 2026, 8:03 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38a4fa6a048190ae6e8e777677e26b completed June 22, 2026, 2:59 a.m.
NEDg Description generation batch_6a38a59a0184819080e951c76a48eb0c completed June 22, 2026, 3:01 a.m.
NED2 Entity disambiguation (via description) batch_6a38a63caecc8190a4ac4eb8af4bb18b completed June 22, 2026, 3:04 a.m.
Created at: May 3, 2026, 4:06 p.m.