Triple

T19437670
Position Surface form Disambiguated ID Type / Status
Subject Live Oak E486264 entity
Predicate hasNearbyCommunity P4647 FINISHED
Object Dowling Park
Dowling Park is a small unincorporated riverside community in Suwannee County, Florida, known for its retirement and residential facilities near the town of Live Oak.
E1737919 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: Dowling Park | Statement: [Live Oak, hasNearbyCommunity, Dowling Park]
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: Dowling Park
Triple: [Live Oak, hasNearbyCommunity, Dowling Park]
Generated description
Dowling Park is a small unincorporated riverside community in Suwannee County, Florida, known for its retirement and residential facilities near the town of Live Oak.

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_69d8e8d7ad488190a3373045029b0f3b completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e6336281b88190b1e5ad2606d7c314 completed April 20, 2026, 2:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11fe39a89c81909f3a19e02ddb72ed completed May 23, 2026, 7:21 p.m.
NEDg Description generation batch_6a11ff4907e88190aaad22b7390bc094 completed May 23, 2026, 7:26 p.m.
NED2 Entity disambiguation (via description) batch_6a1200461b94819098a2cbd8b03d4076 completed May 23, 2026, 7:30 p.m.
Created at: April 10, 2026, 1:38 p.m.