Triple

T36748196
Position Surface form Disambiguated ID Type / Status
Subject The Burning Plain E907828 entity
Predicate portraysCharacter P1668 FINISHED
Object Kim Basinger as Gina
Kim Basinger as Gina is the role played by the American actress Kim Basinger in the drama film "The Burning Plain," where she portrays a troubled woman entangled in a secret love affair.
E2198270 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: Kim Basinger as Gina | Statement: [The Burning Plain, portraysCharacter, Kim Basinger as Gina]
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: Kim Basinger as Gina
Triple: [The Burning Plain, portraysCharacter, Kim Basinger as Gina]
Generated description
Kim Basinger as Gina is the role played by the American actress Kim Basinger in the drama film "The Burning Plain," where she portrays a troubled woman entangled in a secret love affair.

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_69f76e76d10881909ec1679bc043108c completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f7c940616c8190a04c5fd65a82c778 completed May 3, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3c173560b081909cb828aa41b7215e completed June 24, 2026, 5:43 p.m.
NEDg Description generation batch_6a3c189bc68c8190bdd7e56b3d49f056 completed June 24, 2026, 5:49 p.m.
NED2 Entity disambiguation (via description) batch_6a3c56fc3dec81908c85d734cc6dbee2 completed June 24, 2026, 10:15 p.m.
Created at: May 3, 2026, 4:12 p.m.