Triple
T12347113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vouvray wines |
E294383
|
entity |
| Predicate | mayBeAffectedBy |
P63713
|
FINISHED |
| Object | noble rot |
—
|
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: noble rot | Statement: [Vouvray wines, mayBeAffectedBy, noble rot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayBeAffectedBy Context triple: [Vouvray wines, mayBeAffectedBy, noble rot]
-
A.
areAffectedBy
Indicates that one entity experiences an effect, influence, or impact as a result of another entity or event.
-
B.
hasPossibleInfluence
chosen
Indicates that one entity may have an effect on, contribute to, or shape the state, behavior, or outcome of another entity, without asserting that this influence is definite or direct.
-
C.
mayBeModifiedBy
Indicates that an entity has the potential to be altered, changed, or updated by another entity or process.
-
D.
hasUpstreamImpactOn
Indicates that one entity’s actions, changes, or outputs affect another entity that is positioned earlier in a process, flow, or dependency chain.
-
E.
hasImplicationsFor
Indicates that one entity’s state, action, or condition leads to consequences, effects, or relevance for another entity.
- 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_69d6ab6ccbec8190b09e2d357aa80064 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f7ba17481908b03af7316b28d9b |
completed | April 10, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69d93ecb5efc819086a3530282278bb1 |
completed | April 10, 2026, 6:17 p.m. |
Created at: April 8, 2026, 9:53 p.m.