Triple

T25556111
Position Surface form Disambiguated ID Type / Status
Subject Berkeley Heights Public Schools E640571 entity
Predicate hasSchool P113 FINISHED
Object William Woodruff Elementary School
William Woodruff Elementary School is a public elementary school serving young students in the Berkeley Heights Public Schools district in New Jersey.
E650289 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: William Woodruff Elementary School | Statement: [Berkeley Heights Public Schools, hasSchool, William Woodruff Elementary School]
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: William Woodruff Elementary School
Triple: [Berkeley Heights Public Schools, hasSchool, William Woodruff Elementary School]
Generated description
William Woodruff Elementary School is a public elementary school serving young students in the Berkeley Heights Public Schools district in New Jersey.

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_69e75dc101a881909fd33b02174e9768 completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5f8ca2cf48190997cd68875571217 completed May 2, 2026, 1:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1127224a1c819090fef6e11d377d49 completed May 23, 2026, 4:03 a.m.
NEDg Description generation batch_6a1134428eb48190a9876894c5ec41da completed May 23, 2026, 4:59 a.m.
NED2 Entity disambiguation (via description) batch_6a1134f4d4d8819087e2dcc8f2f87909 completed May 23, 2026, 5:02 a.m.
Created at: April 21, 2026, 3:39 p.m.