Triple
T3492283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tiaguinho |
E73760
|
entity |
| Predicate | culturalConnotation |
P4340
|
FINISHED |
| Object | familiarity |
—
|
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: familiarity | Statement: [Tiaguinho, culturalConnotation, familiarity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: culturalConnotation Context triple: [Tiaguinho, culturalConnotation, familiarity]
-
A.
hasCulturalConcept
Indicates that an entity embodies, includes, or is associated with a particular cultural idea, value, practice, or construct.
-
B.
culturalEmphasis
Indicates that a culture, group, or context places particular importance or priority on a specific value, practice, or domain.
-
C.
hasConnotation
chosen
Indicates that one entity carries an implied or associated meaning, tone, or emotional nuance in relation to another entity.
-
D.
culturalVariation
Indicates that there are differences in practices, beliefs, or expressions between cultures or within a culture across groups, contexts, or time.
-
E.
culturalCategory
Indicates that one entity classifies or groups another entity according to a particular culture, tradition, or culturally defined type.
- 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_69ad85cca8d4819088494e9f3340fab5 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbabd1708190b1ca4cbc87462b5b |
completed | March 8, 2026, 6:10 p.m. |
| PD | Predicate disambiguation | batch_69adae0b34908190b2bb5766a2231f7a |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:18 p.m.