Triple

T31718451
Position Surface form Disambiguated ID Type / Status
Subject Annie Dillard E809507 entity
Predicate notableWork P4 FINISHED
Object An American Childhood
An American Childhood is Annie Dillard’s lyrical memoir reflecting on her girlhood in mid-20th-century Pittsburgh and the development of her keen powers of observation and imagination.
 E1973886 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 American Childhood | Statement: [Annie Dillard, notableWork, An American Childhood]
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 American Childhood
Triple: [Annie Dillard, notableWork, An American Childhood]
Generated description
An American Childhood is Annie Dillard’s lyrical memoir reflecting on her girlhood in mid-20th-century Pittsburgh and the development of her keen powers of observation and imagination.

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_69f348df4e048190a4a5a9932ada78d6 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6aaf5de6c81908c973e2398444e42 completed May 3, 2026, 1:55 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b84d6eb9481909b2a6ce485f6f959 completed June 12, 2026, 4:02 a.m.
NEDg Description generation batch_6a2b85e4fc608190a551908892ac4ef6 completed June 12, 2026, 4:07 a.m.
NED2 Entity disambiguation (via description) batch_6a2b864e61fc81908ed2c67d91e51164 completed June 12, 2026, 4:08 a.m.
Created at: April 30, 2026, 11:18 p.m.