Triple

T24662931
Position Surface form Disambiguated ID Type / Status
Subject Stone Harbor Elementary School E610586 entity
Predicate locatedIn P40 FINISHED
Object Stone Harbor School District
Stone Harbor School District is a small public school district in Stone Harbor, New Jersey, that provides elementary education to local students.
E1644954 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: Stone Harbor School District | Statement: [Stone Harbor Elementary School, locatedIn, Stone Harbor School District]
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: Stone Harbor School District
Triple: [Stone Harbor Elementary School, locatedIn, Stone Harbor School District]
Generated description
Stone Harbor School District is a small public school district in Stone Harbor, New Jersey, that provides elementary education to local students.

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_69e2c4d453248190a020354e93ef6282 completed April 17, 2026, 11:40 p.m.
NER Named-entity recognition batch_69f40f99e0248190aab8a98dfb98dd16 completed May 1, 2026, 2:27 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1004a069e48190829fe20e8d460f16 completed May 22, 2026, 7:24 a.m.
NEDg Description generation batch_6a100675fa68819087b9ec01a022a5ee completed May 22, 2026, 7:32 a.m.
NED2 Entity disambiguation (via description) batch_6a10073968648190a5ff33d17b5b2984 completed May 22, 2026, 7:35 a.m.
Created at: April 18, 2026, 2:34 a.m.