Triple

T32954484
Position Surface form Disambiguated ID Type / Status
Subject Winona Hawkins E843058 entity
Predicate child P120 FINISHED
Object Willa Givens
Willa Givens is the daughter of U.S. Marshal Raylan Givens and his ex-wife Winona Hawkins in the television series "Justified."
E2041046 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: Willa Givens | Statement: [Winona Hawkins, child, Willa Givens]
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: Willa Givens
Triple: [Winona Hawkins, child, Willa Givens]
Generated description
Willa Givens is the daughter of U.S. Marshal Raylan Givens and his ex-wife Winona Hawkins in the television series "Justified."

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_69f3494a31f481909057136e49b4fe60 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d17171388190b37fb7268a80b569 completed May 3, 2026, 4:39 a.m.
NED1 Entity disambiguation (via context triple) batch_6a352fa2c5a881909be199e85823f06e completed June 19, 2026, 12:01 p.m.
NEDg Description generation batch_6a3530cd95308190985534d24bb1dc20 completed June 19, 2026, 12:06 p.m.
NED2 Entity disambiguation (via description) batch_6a3531bf1bfc8190a82a471b89b4f260 completed June 19, 2026, 12:10 p.m.
Created at: May 1, 2026, 1:21 a.m.