Triple

T30847781
Position Surface form Disambiguated ID Type / Status
Subject Valley Cottage, New York E785685 entity
Predicate hasLibrary P35 FINISHED
Object Valley Cottage Free Library
Valley Cottage Free Library is a public library serving the residents of Valley Cottage, New York, providing access to books, media, and community programs.
E1932583 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: Valley Cottage Free Library | Statement: [Valley Cottage, New York, hasLibrary, Valley Cottage Free Library]
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: Valley Cottage Free Library
Triple: [Valley Cottage, New York, hasLibrary, Valley Cottage Free Library]
Generated description
Valley Cottage Free Library is a public library serving the residents of Valley Cottage, New York, providing access to books, media, and community programs.

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_69f224b850848190a4af4ccf8ddadcdf completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f6917961ec81908dbd73e67c1ff383 completed May 3, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_6a28bbf8bdac819088923a49b26dbf84 completed June 10, 2026, 1:20 a.m.
NEDg Description generation batch_6a28bc6cb6a881909ed7d6cc6f4d697d completed June 10, 2026, 1:22 a.m.
NED2 Entity disambiguation (via description) batch_6a28bd13dd1481908f54532c8d0f9e47 completed June 10, 2026, 1:25 a.m.
Created at: April 29, 2026, 8:46 p.m.