Triple
T18394202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Tapissier de Notre-Dame |
E449820
|
entity |
| Predicate | hasIronicConnotation |
P71901
|
FINISHED |
| Object | true |
—
|
LITERAL 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: true | Statement: [Le Tapissier de Notre-Dame, hasIronicConnotation, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIronicConnotation Context triple: [Le Tapissier de Notre-Dame, hasIronicConnotation, true]
-
A.
hasIronicMeaning
chosen
Indicates that something conveys a meaning opposite to or incongruent with its literal expression, creating an ironic effect.
-
B.
hasConnotation
Indicates that one entity carries an implied or associated meaning, tone, or emotional nuance in relation to another entity.
-
C.
hasPositiveConnotation
Indicates that something is associated with favorable, pleasant, or approving meanings, feelings, or implications.
-
D.
hasOppositeConnotationInModernUsage
Indicates that one concept or expression now carries a meaning or emotional tone in contemporary usage that is opposite to that of another.
-
E.
usedHyperbolically
Indicates that the action or property is being expressed with deliberate exaggeration for emphasis or effect, rather than as a literal statement.
- F. None of above.
Provenance (3 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_69d8b9fab8a8819086a9ddc0871715e0 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e51845d6708190bc96ec801e21b7a3 |
completed | April 19, 2026, 6 p.m. |
| PD | Predicate disambiguation | batch_69e44ff1f92c8190afbb8e85d12bf2a9 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:46 a.m.