Triple
T11239677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murray Franklin |
E266036
|
entity |
| Predicate | portrayedByCharacterAgeApprox |
P98619
|
FINISHED |
| Object | late 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: late middle-aged man | Statement: [Murray Franklin, portrayedByCharacterAgeApprox, late middle-aged man]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayedByCharacterAgeApprox Context triple: [Murray Franklin, portrayedByCharacterAgeApprox, late 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.
portrayedByAlsoPlays
Indicates that the actor who portrays a given character also plays another specified role or character.
-
D.
playedBy
Indicates that a role, character, or performance is portrayed or executed by a specific person or agent.
-
E.
portraysYoungerVersionOfCharacterFrom
Indicates that one character is depicted as a younger version of another character from a specified source.
- 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_69d6aac656d48190b275efaa7d6074ee |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e918375081908c2a7ccb50cbf331 |
completed | April 9, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69d7878906f48190b63ddc103a0c8f9b |
completed | April 9, 2026, 11:03 a.m. |
| PDg | Predicate description generation | batch_69d796cf74308190a5b29d0dd82954a2 |
completed | April 9, 2026, 12:08 p.m. |
Created at: April 8, 2026, 9:30 p.m.