Triple

T37066356
Position Surface form Disambiguated ID Type / Status
Subject Sharon Woods Park E917453 entity
Predicate hasRecreationFacility P6792 FINISHED
Object Sharon Woods Golf Course
Sharon Woods Golf Course is a public 18-hole golf course located within Sharon Woods Park, offering recreational golfing in a scenic natural setting.
E2214385 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: Sharon Woods Golf Course | Statement: [Sharon Woods Park, hasRecreationFacility, Sharon Woods Golf Course]
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: Sharon Woods Golf Course
Triple: [Sharon Woods Park, hasRecreationFacility, Sharon Woods Golf Course]
Generated description
Sharon Woods Golf Course is a public 18-hole golf course located within Sharon Woods Park, offering recreational golfing in a scenic natural setting.

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_69f76e95fa40819091e14681087ae5e4 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb2f906d6c8190965fe859102817c1 completed May 6, 2026, 12:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3f6a069e9881908f9bc4f81da20aae completed June 27, 2026, 6:13 a.m.
NEDg Description generation batch_6a3f6ad89c6c81908b2526a3098c3a12 completed June 27, 2026, 6:16 a.m.
NED2 Entity disambiguation (via description) batch_6a3f6b57be6c819080ba84bcb71ec152 completed June 27, 2026, 6:19 a.m.
Created at: May 3, 2026, 4:14 p.m.