Triple

T30007387
Position Surface form Disambiguated ID Type / Status
Subject The Emoji Movie E762361 entity
Predicate editor P1954 FINISHED
Object William J. Caparella
William J. Caparella is a film editor best known for his work on the animated feature "The Emoji Movie."
E1976849 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: William J. Caparella | Statement: [The Emoji Movie, editor, William J. Caparella]
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: William J. Caparella
Triple: [The Emoji Movie, editor, William J. Caparella]
Generated description
William J. Caparella is a film editor best known for his work on the animated feature "The Emoji Movie."

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_69f2246a47ac81909cf5213053687ffc completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f67953393481909fce15619f1a1c10 completed May 2, 2026, 10:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2b944b6a888190ab7bd17973cfc860 completed June 12, 2026, 5:08 a.m.
NEDg Description generation batch_6a2b9629df688190934777968725ed22 completed June 12, 2026, 5:16 a.m.
NED2 Entity disambiguation (via description) batch_6a2b96a8ddb48190a66527e08cdcf3c1 completed June 12, 2026, 5:18 a.m.
Created at: April 29, 2026, 6:43 p.m.