Triple
T21751876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fleur de Figuier |
E536933
|
entity |
| Predicate | hasSensoryAssociation |
P93007
|
FINISHED |
| Object | freshness |
—
|
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: freshness | Statement: [Fleur de Figuier, hasSensoryAssociation, freshness]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSensoryAssociation Context triple: [Fleur de Figuier, hasSensoryAssociation, freshness]
-
A.
sensoryModality
Indicates the type of sensory channel (e.g., visual, auditory, tactile) through which an experience, perception, or information is received or processed.
-
B.
hasSensation
chosen
Indicates that an entity experiences or is subject to a particular sensory or perceptual feeling.
-
C.
sensorySystem
Indicates that one entity functions as the sensory system (or part of it) of another, enabling the detection and processing of internal or external stimuli.
-
D.
sensoryCommunication
Indicates a relationship where one entity conveys or exchanges information with another through sensory signals or perception-based means.
-
E.
hasSensoryOrgans
Indicates that an entity possesses organs specialized for sensing or perceiving stimuli from its environment.
- 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_69e0c46eab808190b848242d63a17c47 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01d8a6d4881908cc69e7247cce3a5 |
completed | April 28, 2026, 2:38 a.m. |
| PD | Predicate disambiguation | batch_69e6969c16fc8190b5126c169317d85d |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:50 p.m.