Triple

T35208320
Position Surface form Disambiguated ID Type / Status
Subject Pro Wrestling Illustrated Woman of the Year E1016601 entity
Predicate hasRelatedList P26016 FINISHED
Object PWI Female 50
PWI Female 50 is Pro Wrestling Illustrated’s annual ranked list highlighting the top 50 women’s professional wrestlers in the world for a given year.
 E2131848 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: PWI Female 50 | Statement: [Pro Wrestling Illustrated Woman of the Year, hasRelatedList, PWI Female 50]
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: PWI Female 50
Triple: [Pro Wrestling Illustrated Woman of the Year, hasRelatedList, PWI Female 50]
Generated description
PWI Female 50 is Pro Wrestling Illustrated’s annual ranked list highlighting the top 50 women’s professional wrestlers in the world for a given year.

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_69f76ddf549c8190869d0af076fd2c28 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78e70a4408190b238e834946941db completed May 3, 2026, 6:05 p.m.
NED1 Entity disambiguation (via context triple) batch_6a380406450481909d89f33c59aa7da1 completed June 21, 2026, 3:32 p.m.
NEDg Description generation batch_6a3804ebed608190995d50cb0cf6243a completed June 21, 2026, 3:36 p.m.
NED2 Entity disambiguation (via description) batch_6a380598bfd48190a3d7d541ff5d5cde completed June 21, 2026, 3:39 p.m.
Created at: May 3, 2026, 4:02 p.m.