Triple

T33530678
Position Surface form Disambiguated ID Type / Status
Subject Hank Mitchell E858769 entity
Predicate narrativeRole P268 FINISHED
Object unreliable narrator (novel)
An unreliable narrator (novel) is a story’s first-person voice whose limited knowledge, bias, or deceit makes their account of events untrustworthy, forcing readers to question and interpret the truth for themselves.
 E2055071 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: unreliable narrator (novel) | Statement: [Hank Mitchell, narrativeRole, unreliable narrator (novel)]
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: unreliable narrator (novel)
Triple: [Hank Mitchell, narrativeRole, unreliable narrator (novel)]
Generated description
An unreliable narrator (novel) is a story’s first-person voice whose limited knowledge, bias, or deceit makes their account of events untrustworthy, forcing readers to question and interpret the truth for themselves.

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_69f34978caf4819083f90eba4944d8e8 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6f6a4a0988190b314f9362f18e455 completed May 3, 2026, 7:17 a.m.
NED1 Entity disambiguation (via context triple) batch_6a35a67ec2188190ba3f57e2fa763c48 completed June 19, 2026, 8:28 p.m.
NEDg Description generation batch_6a35a74280e481908a5e8d58159ccf14 completed June 19, 2026, 8:32 p.m.
NED2 Entity disambiguation (via description) batch_6a35a7da68bc819090b95df78ec28e57 completed June 19, 2026, 8:34 p.m.
Created at: May 1, 2026, 1:39 a.m.