Triple

T23942664
Position Surface form Disambiguated ID Type / Status
Subject Leelee Sobieski E602824 entity
Predicate notableWork P4 FINISHED
Object A Soldier’s Daughter Never Cries
A Soldier’s Daughter Never Cries is a 1998 drama film, based on Kaylie Jones’s semi-autobiographical novel, about an American family living in Paris and later returning to the United States.
 E1609272 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: A Soldier’s Daughter Never Cries | Statement: [Leelee Sobieski, notableWork, A Soldier’s Daughter Never Cries]
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: A Soldier’s Daughter Never Cries
Triple: [Leelee Sobieski, notableWork, A Soldier’s Daughter Never Cries]
Generated description
A Soldier’s Daughter Never Cries is a 1998 drama film, based on Kaylie Jones’s semi-autobiographical novel, about an American family living in Paris and later returning to the United States.

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_69e2953e4924819093f1c24c03476b42 completed April 17, 2026, 8:17 p.m.
NER Named-entity recognition batch_69f1d02ae4e4819083c0e160395b6fea completed April 29, 2026, 9:32 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f764eb038819095207e3cbb948f07 completed May 21, 2026, 9:17 p.m.
NEDg Description generation batch_6a0f7721b65481908b58b4d68e50768c completed May 21, 2026, 9:20 p.m.
NED2 Entity disambiguation (via description) batch_6a0f788c4c108190b79e1ea898be2a80 completed May 21, 2026, 9:26 p.m.
Created at: April 17, 2026, 9:10 p.m.