Triple

T27694183
Position Surface form Disambiguated ID Type / Status
Subject Papago Park E698242 entity
Predicate hasAttraction P105 FINISHED
Object Papago Golf Course
Papago Golf Course is a popular public golf facility in Phoenix, Arizona, known for its scenic desert landscape and views of the distinctive red sandstone buttes of Papago Park.
E1784201 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: Papago Golf Course | Statement: [Papago Park, hasAttraction, Papago 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: Papago Golf Course
Triple: [Papago Park, hasAttraction, Papago Golf Course]
Generated description
Papago Golf Course is a popular public golf facility in Phoenix, Arizona, known for its scenic desert landscape and views of the distinctive red sandstone buttes of Papago Park.

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_69ef590ea74081908f0cd7500d85fa27 completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f6359e3d3c81909814e2f0a7fb0ea9 completed May 2, 2026, 5:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12dab8d6fc8190b0342502b71bc92e completed May 24, 2026, 11:02 a.m.
NEDg Description generation batch_6a12db2833688190af921e97c6e5d05d completed May 24, 2026, 11:04 a.m.
NED2 Entity disambiguation (via description) batch_6a12db977df48190b71bce8408b51269 completed May 24, 2026, 11:05 a.m.
Created at: April 27, 2026, 2:53 p.m.