Triple

T32980437
Position Surface form Disambiguated ID Type / Status
Subject Land O' Lakes, Florida E843780 entity
Predicate hasPark P105 FINISHED
Object Land O' Lakes Recreation Complex
Land O' Lakes Recreation Complex is a community sports and recreation facility in Land O' Lakes, Florida, featuring athletic fields, courts, and family-oriented amenities.
E2031725 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: Land O' Lakes Recreation Complex | Statement: [Land O' Lakes, Florida, hasPark, Land O' Lakes Recreation Complex]
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: Land O' Lakes Recreation Complex
Triple: [Land O' Lakes, Florida, hasPark, Land O' Lakes Recreation Complex]
Generated description
Land O' Lakes Recreation Complex is a community sports and recreation facility in Land O' Lakes, Florida, featuring athletic fields, courts, and family-oriented amenities.

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_69f3494c6f9c8190a255409fce8b1d3b completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d1dbc8508190afb60638af280c54 completed May 3, 2026, 4:40 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34dabda4148190a3a5261e08975e89 completed June 19, 2026, 5:59 a.m.
NEDg Description generation batch_6a34dbb1e474819095ca57b4364327cd completed June 19, 2026, 6:03 a.m.
NED2 Entity disambiguation (via description) batch_6a34dc3d2df08190932ef2da9ac631ae completed June 19, 2026, 6:05 a.m.
Created at: May 1, 2026, 1:22 a.m.