Triple
T1943162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lulu |
E41999
|
entity |
| Predicate | perceivedConnotation |
P4340
|
FINISHED |
| Object | cute |
—
|
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: cute | Statement: [Lulu, perceivedConnotation, cute]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: perceivedConnotation Context triple: [Lulu, perceivedConnotation, cute]
-
A.
hasConnotation
chosen
Indicates that one entity carries an implied or associated meaning, tone, or emotional nuance in relation to another entity.
-
B.
influencedPerceptionOf
Indicates that one entity has affected, shaped, or altered how another entity is perceived or understood.
-
C.
possibleMeaning
Indicates that something may plausibly represent, signify, or be interpreted as a particular meaning or sense.
-
D.
commonMeaning
Indicates that multiple entities share the same or very similar meaning or semantic interpretation.
-
E.
hasConceptualOpposite
Indicates that one entity represents a concept that is fundamentally opposed or contrary in meaning to the concept represented by 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_69a8870e08fc8190a319cbf2600db15f |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb32ebae881908f7541301f0198ae |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abaff25a588190bb4cbc8df9fc6d64 |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:36 p.m.