Triple
T15337483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Warne |
E366703
|
entity |
| Predicate | portrayedByCharacterAgeApproximation |
P98619
|
FINISHED |
| Object | adult |
—
|
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: adult | Statement: [Peter Warne, portrayedByCharacterAgeApproximation, adult]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayedByCharacterAgeApproximation Context triple: [Peter Warne, portrayedByCharacterAgeApproximation, adult]
-
A.
portrayedByCharacterAgeApprox
chosen
Indicates that an entity is portrayed by a character whose age is approximately a specified value or age range.
-
B.
portrayedBy
Indicates that one entity serves as the actor or performer who represents or plays the role of another entity in a work or medium.
-
C.
portraysFromAge
Indicates that one entity depicts another entity starting from a specified age of the depicted entity.
-
D.
youngerVersionPortrayedBy
Indicates that one person portrays a younger version of another person, typically in a film, television show, or similar narrative work.
-
E.
portrayedByAlsoPlays
Indicates that the actor who portrays a given character also plays another specified role or character.
- 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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e11b22c81908280efe65acd5454 |
completed | April 16, 2026, 1:40 a.m. |
| PD | Predicate disambiguation | batch_69deca9659f48190b8661df223ce5078 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:17 a.m.