Triple

T34010469

Position	Surface form	Disambiguated ID	Type / Status
Subject	Quite Interesting	`E872095`	entity
Predicate	hasRegularRound	`P205277`	FINISHED
Object	General Ignorance General Ignorance is a recurring quiz round on the British television panel show "QI" that humorously exposes and corrects common misconceptions and widely held false beliefs.	`E2077631`	NE FINISHED

How this triple was built (3 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: General Ignorance | Statement: [Quite Interesting, hasRegularRound, General Ignorance]

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: General Ignorance
Triple: [Quite Interesting, hasRegularRound, General Ignorance]

Generated description

General Ignorance is a recurring quiz round on the British television panel show "QI" that humorously exposes and corrects common misconceptions and widely held false beliefs.

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: hasRegularRound
Context triple: [Quite Interesting, hasRegularRound, General Ignorance]

A. hasRound
Indicates that an entity possesses, includes, or is associated with a particular round (e.g., a round of an event, game, or process).
B. hasProperRounds
Indicates that an entity is associated with rounds that meet specified standards or criteria for being considered proper or valid.
C. hasRoudNumber
Indicates that an entity is associated with or assigned a specific round number within a sequence of rounds or stages.
D. hasRoundStructure
Indicates that one entity possesses or includes a structure that is circular or rounded in shape.
E. hasNonTitleRounds
Indicates that an event, competition, or series includes rounds that are not designated as title or championship rounds.
F. None of above. chosen

Provenance (7 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_69f349a08848819084b348d64c1879c3`	completed	April 30, 2026, 12:22 p.m.
NER	Named-entity recognition	`batch_6a037c92f03c8190ae2751270b195423`	completed	May 12, 2026, 7:16 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a3692ed74408190ab19abe0286eb0d2`	completed	June 20, 2026, 1:17 p.m.
NEDg	Description generation	`batch_6a3693c6c72081908b00643cc42b85d1`	completed	June 20, 2026, 1:21 p.m.
NED2	Entity disambiguation (via description)	`batch_6a3694b83d048190a29c179e9407f41f`	completed	June 20, 2026, 1:25 p.m.
PD	Predicate disambiguation	`batch_6a0379f963908190846d232f386fd98f`	completed	May 12, 2026, 7:05 p.m.
PDg	Predicate description generation	`batch_6a037c80ba448190853011097a151b7e`	completed	May 12, 2026, 7:16 p.m.

Created at: May 1, 2026, 1:51 a.m.