Triple

T34980260
Position Surface form Disambiguated ID Type / Status
Subject South Bend Blue Sox E1008793 entity
Predicate notablePlayer P304 FINISHED
Object Earlene Risinger
Earlene Risinger was a standout pitcher in the All-American Girls Professional Baseball League, best known for her tenure with the South Bend Blue Sox in the early 1950s.
E2195527 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: Earlene Risinger | Statement: [South Bend Blue Sox, notablePlayer, Earlene Risinger]
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: Earlene Risinger
Triple: [South Bend Blue Sox, notablePlayer, Earlene Risinger]
Generated description
Earlene Risinger was a standout pitcher in the All-American Girls Professional Baseball League, best known for her tenure with the South Bend Blue Sox in the early 1950s.

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_69f76dc844a48190881951fffb83d17e completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78498be208190b7f204fbe5388d0a completed May 3, 2026, 5:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3a38008cf481909bbbc8ec96f393e7 completed June 23, 2026, 7:38 a.m.
NEDg Description generation batch_6a3a39f576748190b0cb18e8e85c59fb completed June 23, 2026, 7:47 a.m.
NED2 Entity disambiguation (via description) batch_6a3a3b877df4819095fde5dba8c3b324 completed June 23, 2026, 7:53 a.m.
Created at: May 3, 2026, 4:01 p.m.