Triple
T2490548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stringer Bell |
E52028
|
entity |
| Predicate | portrayalAccent |
P14722
|
FINISHED |
| Object | American accent by British actor Idris Elba |
—
|
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: American accent by British actor Idris Elba | Statement: [Stringer Bell, portrayalAccent, American accent by British actor Idris Elba]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayalAccent Context triple: [Stringer Bell, portrayalAccent, American accent by British actor Idris Elba]
-
A.
portrayalRecognition
Indicates that one entity recognizes or identifies another entity as a portrayal or representation of a particular subject or character.
-
B.
hasAccent
chosen
Indicates that an entity speaks with or possesses a particular accent or distinctive pronunciation style.
-
C.
portrayalReceived
Indicates that an entity has been depicted or represented by another entity, such as through an image, performance, or description.
-
D.
vocalizationCharacteristic
Indicates how an entity’s vocal sounds are characterized, such as their quality, style, or distinctive acoustic features.
-
E.
portrayalLedTo
Indicates that one entity’s portrayal of another caused or significantly contributed to a subsequent outcome, reaction, or state involving that other 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_69ab4955111c8190835bf619adec21ff |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd18fe32081909580c6272a6013c5 |
completed | March 7, 2026, 7:19 a.m. |
| PD | Predicate disambiguation | batch_69abd0b980b481908d4932bcea4a6167 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:45 p.m.