Triple
T23697948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jorge |
E585502
|
entity |
| Predicate | portrayerKnownFor |
P153438
|
FINISHED |
| Object | nuanced roles |
—
|
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: nuanced roles | Statement: [Jorge, portrayerKnownFor, nuanced roles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayerKnownFor Context triple: [Jorge, portrayerKnownFor, nuanced roles]
-
A.
portrayalKnownFor
Indicates that a particular portrayal or role is the one for which an entity is especially recognized or famous.
-
B.
portrayedByAlsoKnownFor
Indicates that an entity is portrayed by a person who is also notably known for another specific role or work.
-
C.
portrayerName
Indicates the name of the person who portrays or plays a particular character or role.
-
D.
portrayedByAlsoPlays
Indicates that the actor who portrays a given character also plays another specified role or character.
-
E.
creatorKnownFor
Indicates that a creator is especially recognized or notable for a particular work, contribution, or achievement.
- F. None of above. chosen
Provenance (4 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_69e24904bd508190abfcb74855de2918 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b67f518481908d0f5b5e75b6ccd4 |
completed | April 29, 2026, 7:42 a.m. |
| PD | Predicate disambiguation | batch_69f155d5265881908e43a9696b6a6d0f |
completed | April 29, 2026, 12:50 a.m. |
| PDg | Predicate description generation | batch_69f157cc43a881909ed2d8b0a09b5d73 |
completed | April 29, 2026, 12:58 a.m. |
Created at: April 17, 2026, 6:53 p.m.