Triple

T34815414
Position Surface form Disambiguated ID Type / Status
Subject Our Hearts Were Young and Gay E1003616 entity
Predicate basedOnAuthor P2806 FINISHED
Object Emily Kimbrough
Emily Kimbrough was an American writer and humorist best known for co-authoring the popular travel memoir "Our Hearts Were Young and Gay."
E2164846 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: Emily Kimbrough | Statement: [Our Hearts Were Young and Gay, basedOnAuthor, Emily Kimbrough]
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: Emily Kimbrough
Triple: [Our Hearts Were Young and Gay, basedOnAuthor, Emily Kimbrough]
Generated description
Emily Kimbrough was an American writer and humorist best known for co-authoring the popular travel memoir "Our Hearts Were Young and Gay."

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_69f76db717088190811b4e744610f37d completed May 3, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f77ab8abd08190a7f001927cc76a8d completed May 3, 2026, 4:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38bfb7473c8190a5bf0fffbbb75320 completed June 22, 2026, 4:53 a.m.
NEDg Description generation batch_6a38c0a8a0908190a845f3f6e7040e1e completed June 22, 2026, 4:57 a.m.
NED2 Entity disambiguation (via description) batch_6a38c164f0e88190bef255d462f21732 completed June 22, 2026, 5 a.m.
Created at: May 3, 2026, 3:59 p.m.