Triple

T24920316
Position Surface form Disambiguated ID Type / Status
Subject Jump and Other Stories E624102 entity
Predicate hasPart P35 FINISHED
Object Once Upon a Time
"Once Upon a Time" is a short story by Nadine Gordimer, known for its allegorical critique of apartheid-era fears and social divisions in South Africa.
 E1657116 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: Once Upon a Time | Statement: [Jump and Other Stories, hasPart, Once Upon a Time]
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: Once Upon a Time
Triple: [Jump and Other Stories, hasPart, Once Upon a Time]
Generated description
"Once Upon a Time" is a short story by Nadine Gordimer, known for its allegorical critique of apartheid-era fears and social divisions in South Africa.

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_69e2fac889c081908e9ff686cb428e5a completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f4238f98ec8190a4159dcec666fe5a completed May 1, 2026, 3:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a103332581c81908e35c5a73b23b758 completed May 22, 2026, 10:42 a.m.
NEDg Description generation batch_6a1033edb6848190b35070d8784af90e completed May 22, 2026, 10:46 a.m.
NED2 Entity disambiguation (via description) batch_6a103487a09c81908960296ff597228f completed May 22, 2026, 10:48 a.m.
Created at: April 18, 2026, 5:28 a.m.