Triple

T35066174
Position Surface form Disambiguated ID Type / Status
Subject Agent Owen E1011735 entity
Predicate portrayedByFullName P1507 FINISHED
Object Julian David Morris
Julian David Morris is a British actor known for his roles in television series such as "Pretty Little Liars," "New Girl," and "Once Upon a Time," as well as various film appearances.
E2126310 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: Julian David Morris | Statement: [Agent Owen, portrayedByFullName, Julian David Morris]
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: Julian David Morris
Triple: [Agent Owen, portrayedByFullName, Julian David Morris]
Generated description
Julian David Morris is a British actor known for his roles in television series such as "Pretty Little Liars," "New Girl," and "Once Upon a Time," as well as various film appearances.

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_69f76dd193108190af2528186f25b72a completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78613f9dc8190b20a15c22090d27f completed May 3, 2026, 5:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37cfe37a808190ae7843090f30430d completed June 21, 2026, 11:49 a.m.
NEDg Description generation batch_6a37d0c6633c81909a7d803ece43f548 completed June 21, 2026, 11:53 a.m.
NED2 Entity disambiguation (via description) batch_6a37d3792f2c81909ae28634bbc0c47a completed June 21, 2026, 12:05 p.m.
Created at: May 3, 2026, 4:01 p.m.