Triple

T35955529
Position Surface form Disambiguated ID Type / Status
Subject The Heat of the Day E1039847 entity
Predicate mainCharacter P1183 FINISHED
Object Robert Kelway
Robert Kelway is a central figure in Elizabeth Bowen’s World War II novel "The Heat of the Day," portrayed as a complex, secretive lover whose suspected treachery drives the story’s tension and moral ambiguity.
E2176381 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: Robert Kelway | Statement: [The Heat of the Day, mainCharacter, Robert Kelway]
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: Robert Kelway
Triple: [The Heat of the Day, mainCharacter, Robert Kelway]
Generated description
Robert Kelway is a central figure in Elizabeth Bowen’s World War II novel "The Heat of the Day," portrayed as a complex, secretive lover whose suspected treachery drives the story’s tension and moral ambiguity.

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_69f76e25ea488190b7cee970b3e70382 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7abdb52dc81909c1ec4b660623cc5 completed May 3, 2026, 8:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a396df05ad88190bb73085a73c28144 completed June 22, 2026, 5:16 p.m.
NEDg Description generation batch_6a396ebb4224819081b9d1f60d22b488 completed June 22, 2026, 5:19 p.m.
NED2 Entity disambiguation (via description) batch_6a3970fbbc608190bf438b9446a9be3d completed June 22, 2026, 5:29 p.m.
Created at: May 3, 2026, 4:07 p.m.