Triple
T28915252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emotions |
E733341
|
entity |
| Predicate | featuresLatinInfluence |
P84649
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Emotions, featuresLatinInfluence, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresLatinInfluence Context triple: [Emotions, featuresLatinInfluence, true]
-
A.
hasLatinInfluence
chosen
Indicates that one entity exerts or reflects cultural, linguistic, or stylistic influence derived from Latin on another entity.
-
B.
hasSignificantSpanishInfluence
Indicates that one entity has been strongly shaped or notably affected by Spanish culture, language, practices, or presence.
-
C.
ethnicAspect
Indicates a relationship where one entity reflects, involves, or characterizes the ethnic background, identity, or attributes of another entity.
-
D.
hasEthnicInfluence
Indicates that one entity has a cultural, traditional, or ethnic impact on, or contributes to shaping the ethnic character of, another entity.
-
E.
ethnolinguisticInfluence
Indicates how one ethnolinguistic group or language affects, shapes, or contributes to the characteristics, practices, or development of another.
- 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_69f05b0a5cc0819094828367ae204b70 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69fec25f0fc48190b87ab1f9cd1eb0de |
completed | May 9, 2026, 5:13 a.m. |
| PD | Predicate disambiguation | batch_69fec079a770819098df7cc3049df954 |
completed | May 9, 2026, 5:04 a.m. |
Created at: April 28, 2026, 8:14 a.m.