Triple
T29083395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xhosa (Wakandan language in MCU) |
E734037
|
entity |
| Predicate | usedToContrast |
P123206
|
FINISHED |
| Object | English dialogue in MCU |
—
|
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: English dialogue in MCU | Statement: [Xhosa (Wakandan language in MCU), usedToContrast, English dialogue in MCU]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedToContrast Context triple: [Xhosa (Wakandan language in MCU), usedToContrast, English dialogue in MCU]
-
A.
contrastUse
Indicates that one entity is used in opposition or distinction to another to highlight differences between them.
-
B.
providesContrastWith
chosen
Indicates that one entity is used to highlight differences or distinctions when compared with another entity.
-
C.
achievesContrast
Indicates that one entity creates or enhances a visual or conceptual difference relative to another entity.
-
D.
oftenContrastedWith
Indicates that one entity is frequently compared to another in a way that highlights their differences or opposing characteristics.
-
E.
traditionalContrastWith
Indicates a relationship where one tradition, practice, or belief is explicitly set in opposition or difference to another, highlighting their contrasting characteristics.
- 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_69f05b0c0f28819086eae6e84f2ae472 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f6afebd7ec8190ab696f363d84abf0 |
completed | May 3, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69f6aca204148190850a3dc325bc07b7 |
completed | May 3, 2026, 2:02 a.m. |
Created at: April 28, 2026, 10:57 a.m.