Triple

T23093648
Position Surface form Disambiguated ID Type / Status
Subject Munson family E575824 entity
Predicate hasNotableMember P304 FINISHED
Object Helen Munson Williams
Helen Munson Williams was a 19th-century American philanthropist and art patron whose bequests helped establish the Munson-Williams-Proctor Arts Institute in Utica, New York.
E1612515 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: Helen Munson Williams | Statement: [Munson family, hasNotableMember, Helen Munson Williams]
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: Helen Munson Williams
Triple: [Munson family, hasNotableMember, Helen Munson Williams]
Generated description
Helen Munson Williams was a 19th-century American philanthropist and art patron whose bequests helped establish the Munson-Williams-Proctor Arts Institute in Utica, New York.

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_69e245c060b48190a9bd61a47a16db17 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f18de2ed088190971ff08c58b15aad completed April 29, 2026, 4:49 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f7e39e7cc8190a83c00d7b9fccf01 completed May 21, 2026, 9:50 p.m.
NEDg Description generation batch_6a0f7f21e3608190b646947083391923 completed May 21, 2026, 9:54 p.m.
NED2 Entity disambiguation (via description) batch_6a0f7fc9437c8190999551269a49fb65 completed May 21, 2026, 9:57 p.m.
Created at: April 17, 2026, 3:57 p.m.