Triple

T32670577
Position Surface form Disambiguated ID Type / Status
Subject Service Corps of Retired Executives E835279 entity
Predicate alsoKnownAs P39 FINISHED
Object SCORE Association
SCORE Association is a U.S. nonprofit organization that provides free mentoring and education to small business owners and entrepreneurs, primarily through a network of volunteer business professionals.
 E2016865 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: SCORE Association | Statement: [Service Corps of Retired Executives, alsoKnownAs, SCORE Association]
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: SCORE Association
Triple: [Service Corps of Retired Executives, alsoKnownAs, SCORE Association]
Generated description
SCORE Association is a U.S. nonprofit organization that provides free mentoring and education to small business owners and entrepreneurs, primarily through a network of volunteer business professionals.

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_69f349303ccc8190a70d0f6e8a21d3fb completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6c7ac1f288190ab86a6dd0b6491cd completed May 3, 2026, 3:57 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3492bbba008190a75fdc19fdfbb79e completed June 19, 2026, 12:52 a.m.
NEDg Description generation batch_6a34938a58dc8190ab8e23d0b021db8b completed June 19, 2026, 12:55 a.m.
NED2 Entity disambiguation (via description) batch_6a34941dcdbc8190b6549f9be8eb672f completed June 19, 2026, 12:58 a.m.
Created at: May 1, 2026, 1:09 a.m.