Triple
T9341875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caladan |
E224781
|
entity |
| Predicate | culturalTone |
P49759
|
FINISHED |
| Object | sober and conservative |
—
|
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: sober and conservative | Statement: [Caladan, culturalTone, sober and conservative]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: culturalTone Context triple: [Caladan, culturalTone, sober and conservative]
-
A.
culturalEmphasis
Indicates that a culture, group, or context places particular importance or priority on a specific value, practice, or domain.
-
B.
culturalType
Indicates the classification of something according to its cultural category, style, or tradition.
-
C.
culturalVariation
Indicates that there are differences in practices, beliefs, or expressions between cultures or within a culture across groups, contexts, or time.
-
D.
culturalCategory
Indicates that one entity classifies or groups another entity according to a particular culture, tradition, or culturally defined type.
-
E.
contributesToTone
chosen
Indicates that one entity plays a role in shaping, influencing, or determining the overall tone or mood of 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_69ca842993248190a79ab06968994b86 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd4bafa9108190889397614756020d |
completed | April 1, 2026, 4:45 p.m. |
| PD | Predicate disambiguation | batch_69cc7a66aef08190b8d668cff5b04f5f |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:40 p.m.