Triple

T33896136
Position Surface form Disambiguated ID Type / Status
Subject Anna Chancellor E868914 entity
Predicate notableRole P22 FINISHED
Object Lix Storm in The Hour
Lix Storm in *The Hour* is a sharp, world-weary foreign correspondent in the 1950s BBC newsroom drama series, known for her incisive journalism and complex personal history.
 E2073820 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: Lix Storm in The Hour | Statement: [Anna Chancellor, notableRole, Lix Storm in The Hour]
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: Lix Storm in The Hour
Triple: [Anna Chancellor, notableRole, Lix Storm in The Hour]
Generated description
Lix Storm in *The Hour* is a sharp, world-weary foreign correspondent in the 1950s BBC newsroom drama series, known for her incisive journalism and complex personal history.

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_69f34997703c8190866b1d404bce531f completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f7017f668c81909728d5db01e77d10 completed May 3, 2026, 8:04 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36823e736481908aef1d6187062cf3 completed June 20, 2026, 12:06 p.m.
NEDg Description generation batch_6a3683b5ac0881909f03e1e94896067d completed June 20, 2026, 12:12 p.m.
NED2 Entity disambiguation (via description) batch_6a36848c2cd88190b28d40551392741b completed June 20, 2026, 12:16 p.m.
Created at: May 1, 2026, 1:48 a.m.