Triple

T36524607
Position Surface form Disambiguated ID Type / Status
Subject She’s the Man E900265 entity
Predicate character P662 FINISHED
Object Sebastian Hastings
Sebastian Hastings is a central character in the teen romantic comedy film "She’s the Man," known as the twin brother whose identity his sister Viola impersonates to join a boys’ soccer team.
E2187292 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: Sebastian Hastings | Statement: [She’s the Man, character, Sebastian Hastings]
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: Sebastian Hastings
Triple: [She’s the Man, character, Sebastian Hastings]
Generated description
Sebastian Hastings is a central character in the teen romantic comedy film "She’s the Man," known as the twin brother whose identity his sister Viola impersonates to join a boys’ soccer team.

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_69f76e5eedb88190a393b8c623f71dd7 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7c2167f588190bdce9ffd22b19fdf completed May 3, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39dbe38b6881908ec63b48b4f147c1 completed June 23, 2026, 1:05 a.m.
NEDg Description generation batch_6a39de5bb93481909ee74b44eb857a2d completed June 23, 2026, 1:16 a.m.
NED2 Entity disambiguation (via description) batch_6a39df8d99648190afa15118801b0db1 completed June 23, 2026, 1:21 a.m.
Created at: May 3, 2026, 4:11 p.m.