Triple

T36320392
Position Surface form Disambiguated ID Type / Status
Subject Peta Nocona E894314 entity
Predicate child P120 FINISHED
Object Prairie Flower
Prairie Flower was the daughter of Comanche chief Peta Nocona and Cynthia Ann Parker, notable as a symbol of the complex history between Native Americans and Anglo-American settlers in 19th-century Texas.
E2179023 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: Prairie Flower | Statement: [Peta Nocona, child, Prairie Flower]
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: Prairie Flower
Triple: [Peta Nocona, child, Prairie Flower]
Generated description
Prairie Flower was the daughter of Comanche chief Peta Nocona and Cynthia Ann Parker, notable as a symbol of the complex history between Native Americans and Anglo-American settlers in 19th-century Texas.

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_69f76e4d1a788190a6ab6ccca28547a7 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7ba44c87481908d75274f105ee9c4 completed May 3, 2026, 9:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a397d90078c8190a570fea23050061f completed June 22, 2026, 6:23 p.m.
NEDg Description generation batch_6a398695aaa881909942fbe5e82da73c completed June 22, 2026, 7:01 p.m.
NED2 Entity disambiguation (via description) batch_6a39873f05588190bcdbbe2690bf4f16 completed June 22, 2026, 7:04 p.m.
Created at: May 3, 2026, 4:09 p.m.