Triple

T29905939
Position Surface form Disambiguated ID Type / Status
Subject Ward Melville High School E759534 entity
Predicate namedAfter P63 FINISHED
Object Ward Melville
Ward Melville was an American businessman and philanthropist best known for developing the planned community of Stony Brook, New York, and supporting educational and cultural institutions there.
E1888520 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: Ward Melville | Statement: [Ward Melville High School, namedAfter, Ward Melville]
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: Ward Melville
Triple: [Ward Melville High School, namedAfter, Ward Melville]
Generated description
Ward Melville was an American businessman and philanthropist best known for developing the planned community of Stony Brook, New York, and supporting educational and cultural institutions there.

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_69f224600590819085e148a01c056ef6 completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f677556fb48190be1de257613ec04d completed May 2, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26f1ebd2ec8190aaa8d49ccf51d8e9 completed June 8, 2026, 4:46 p.m.
NEDg Description generation batch_6a26f28951d0819093b834f08eff940b completed June 8, 2026, 4:49 p.m.
NED2 Entity disambiguation (via description) batch_6a26f365e1448190bc8539feec582fd7 completed June 8, 2026, 4:52 p.m.
Created at: April 29, 2026, 6:08 p.m.