Triple

T37821326
Position Surface form Disambiguated ID Type / Status
Subject Panora, Iowa E942923 entity
Predicate hasNearbyAttraction P2064 FINISHED
Object Lake Panorama National Golf Course
Lake Panorama National Golf Course is a scenic championship golf course in central Iowa known for its lakeside setting and challenging layout.
E2246903 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: Lake Panorama National Golf Course | Statement: [Panora, Iowa, hasNearbyAttraction, Lake Panorama National 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: Lake Panorama National Golf Course
Triple: [Panora, Iowa, hasNearbyAttraction, Lake Panorama National Golf Course]
Generated description
Lake Panorama National Golf Course is a scenic championship golf course in central Iowa known for its lakeside setting and challenging layout.

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_69f76ee987588190906506e759be5db3 completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fbb1c4c0d481909bc4750b40f27939 completed May 6, 2026, 9:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41041470c4819092a7d29c3a85f333 completed June 28, 2026, 11:23 a.m.
NEDg Description generation batch_6a4104e29abc8190826d9ac7d1dbe4c9 completed June 28, 2026, 11:26 a.m.
NED2 Entity disambiguation (via description) batch_6a4106277e448190bf31165fd8f64020 completed June 28, 2026, 11:31 a.m.
Created at: May 3, 2026, 4:19 p.m.