Triple

T23895703
Position Surface form Disambiguated ID Type / Status
Subject Wayward Sisters E600897 entity
Predicate featuresCharacter P626 FINISHED
Object Donna Hanscum
Donna Hanscum is a cheerful yet tough Midwestern sheriff and monster hunter in the Supernatural universe, known for her prominent role in the "Wayward Sisters" ensemble.
E1957201 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: Donna Hanscum | Statement: [Wayward Sisters, featuresCharacter, Donna Hanscum]
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: Donna Hanscum
Triple: [Wayward Sisters, featuresCharacter, Donna Hanscum]
Generated description
Donna Hanscum is a cheerful yet tough Midwestern sheriff and monster hunter in the Supernatural universe, known for her prominent role in the "Wayward Sisters" ensemble.

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_69e295341ac0819080647f2908af793c completed April 17, 2026, 8:16 p.m.
NER Named-entity recognition batch_69f1cdd9203081909b10820a81c5d9d3 completed April 29, 2026, 9:22 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2a1e014f008190a6f84a9ddd32eca7 completed June 11, 2026, 2:31 a.m.
NEDg Description generation batch_6a2a2823d8408190b62a5e80e6878daf completed June 11, 2026, 3:14 a.m.
NED2 Entity disambiguation (via description) batch_6a2a288b41bc8190bfdc652f18191347 completed June 11, 2026, 3:16 a.m.
Created at: April 17, 2026, 8:25 p.m.