Triple

T37535923
Position Surface form Disambiguated ID Type / Status
Subject Dale Scott E933193 entity
Predicate spouse P13 FINISHED
Object Michael Rausch
Michael Rausch is known as the husband of longtime Major League Baseball umpire Dale Scott, with whom he became one of the first same-sex spouses publicly associated with an active MLB umpire.
E2285459 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: Michael Rausch | Statement: [Dale Scott, spouse, Michael Rausch]
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: Michael Rausch
Triple: [Dale Scott, spouse, Michael Rausch]
Generated description
Michael Rausch is known as the husband of longtime Major League Baseball umpire Dale Scott, with whom he became one of the first same-sex spouses publicly associated with an active MLB umpire.

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_69f76ec999288190ae26ec7b6aea7046 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fba3fac40081909b364a44b1756bb7 completed May 6, 2026, 8:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a45eddadc8c8190b32c19d8af783244 completed July 2, 2026, 4:49 a.m.
NEDg Description generation batch_6a45ef114b2c8190bdfd0d2ee8170f6b completed July 2, 2026, 4:54 a.m.
NED2 Entity disambiguation (via description) batch_6a45efa18bb08190a499c4d2ac9c9b48 completed July 2, 2026, 4:57 a.m.
Created at: May 3, 2026, 4:17 p.m.