Triple
T5453957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doyle Lonnegan |
E122432
|
entity |
| Predicate | portrayedByCharacterAge |
P13483
|
FINISHED |
| Object | middle-aged man |
—
|
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: middle-aged man | Statement: [Doyle Lonnegan, portrayedByCharacterAge, middle-aged man]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayedByCharacterAge Context triple: [Doyle Lonnegan, portrayedByCharacterAge, middle-aged man]
-
A.
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.
-
B.
youngerVersionPortrayedBy
Indicates that one person portrays a younger version of another person, typically in a film, television show, or similar narrative work.
-
C.
portraysAgeGroup
chosen
Indicates that one entity depicts or represents another entity as belonging to a particular age group.
-
D.
characterPortrayedIs
Indicates that one entity serves as the fictional or dramatic role that is depicted or played by another entity.
-
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_69bd46424248819085282ddf50a565f3 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd927c946c8190aef40679199fede3 |
completed | March 20, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69bd91a0d96c8190bd1299edbf764bbb |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:08 p.m.