Triple

T26223044
Position Surface form Disambiguated ID Type / Status
Subject Joseph Estevez E655813 entity
Predicate notableWork P4 FINISHED
Object The Story of Jesus for Children
The Story of Jesus for Children is a Christian film adaptation of the life and teachings of Jesus specifically tailored to a young audience.
E653819 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: The Story of Jesus for Children | Statement: [Joseph Estevez, notableWork, The Story of Jesus for Children]
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: The Story of Jesus for Children
Triple: [Joseph Estevez, notableWork, The Story of Jesus for Children]
Generated description
The Story of Jesus for Children is a Christian film adaptation of the life and teachings of Jesus specifically tailored to a young audience.

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_69ee5b4a77e08190bfcb5f8ecdc55abd completed April 26, 2026, 6:36 p.m.
NER Named-entity recognition batch_69f60d5127d48190b28c89797f2852f2 completed May 2, 2026, 2:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a118591cc448190b0ba8459f813f58c completed May 23, 2026, 10:46 a.m.
NEDg Description generation batch_6a11863dfad48190903b8defdb1f64f6 completed May 23, 2026, 10:49 a.m.
NED2 Entity disambiguation (via description) batch_6a1186d8c7bc81909c851862b3a29e13 completed May 23, 2026, 10:52 a.m.
Created at: April 26, 2026, 8:57 p.m.