Triple

T37247363
Position Surface form Disambiguated ID Type / Status
Subject Wild and Wonderful E923894 entity
Predicate hasCastMember P2308 FINISHED
Object Cheryl Miller
Cheryl Miller is an American former basketball player widely regarded as one of the greatest women’s players in the sport’s history and later a prominent basketball coach and broadcaster.
E137124 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: Cheryl Miller | Statement: [Wild and Wonderful, hasCastMember, Cheryl Miller]
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: Cheryl Miller
Triple: [Wild and Wonderful, hasCastMember, Cheryl Miller]
Generated description
Cheryl Miller is an American former basketball player widely regarded as one of the greatest women’s players in the sport’s history and later a prominent basketball coach and broadcaster.

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_69f76eaabb4c819093b751b139dad551 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb36fd387881909dbabd8f13e6a16d completed May 6, 2026, 12:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a4043cdcb8c8190b060425005b6e2a2 completed June 27, 2026, 9:42 p.m.
NEDg Description generation batch_6a40443a34148190b5b0848559466617 completed June 27, 2026, 9:44 p.m.
NED2 Entity disambiguation (via description) batch_6a404639e5a88190a204ac46e57a660f completed June 27, 2026, 9:52 p.m.
Created at: May 3, 2026, 4:15 p.m.