Triple

T24374916
Position Surface form Disambiguated ID Type / Status
Subject Sanora Babb E614443 entity
Predicate notableWork P4 FINISHED
Object An Owl on Every Post
An Owl on Every Post is a memoir by American writer Sanora Babb that vividly recounts her hardscrabble childhood on the Great Plains in the early 20th century.
 E1632204 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: An Owl on Every Post | Statement: [Sanora Babb, notableWork, An Owl on Every 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: An Owl on Every Post
Triple: [Sanora Babb, notableWork, An Owl on Every Post]
Generated description
An Owl on Every Post is a memoir by American writer Sanora Babb that vividly recounts her hardscrabble childhood on the Great Plains in the early 20th century.

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_69e2d7e1e010819098b95eb3f905943d completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f293d67404819091281523ef12b9b5 completed April 29, 2026, 11:27 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fd6771c8c8190a1fdf6c0773bd30c completed May 22, 2026, 4:07 a.m.
NEDg Description generation batch_6a0fd901edf481908f708d76fe382f8f completed May 22, 2026, 4:18 a.m.
NED2 Entity disambiguation (via description) batch_6a0fd9daf92481908135f46c41cdbbd7 completed May 22, 2026, 4:21 a.m.
Created at: April 18, 2026, 2:02 a.m.