Triple

T33144871
Position Surface form Disambiguated ID Type / Status
Subject Cameron, Arizona E848262 entity
Predicate hasStructure P35 FINISHED
Object Cameron Trading Post
Cameron Trading Post is a historic roadside trading post and lodge near the Grand Canyon in northern Arizona, known for its Native American art, jewelry, and Southwestern goods.
E2037536 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: Cameron Trading Post | Statement: [Cameron, Arizona, hasStructure, Cameron Trading Post]
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: Cameron Trading Post
Triple: [Cameron, Arizona, hasStructure, Cameron Trading Post]
Generated description
Cameron Trading Post is a historic roadside trading post and lodge near the Grand Canyon in northern Arizona, known for its Native American art, jewelry, and Southwestern goods.

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_69f3495a458c8190a1d34b237ba0be3f completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d88921e881909b12898982b137b1 completed May 3, 2026, 5:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a351628265881908da886e4ad0fd9c2 completed June 19, 2026, 10:12 a.m.
NEDg Description generation batch_6a3516dbe1988190a7f496d7b7e8a8b8 completed June 19, 2026, 10:15 a.m.
NED2 Entity disambiguation (via description) batch_6a35178f9d508190abd1a965adb82e98 completed June 19, 2026, 10:18 a.m.
Created at: May 1, 2026, 1:28 a.m.