Triple
T26439330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | portraits of Lord Byron |
E665041
|
entity |
| Predicate | hasDepictedPersonOccupation |
P17608
|
FINISHED |
| Object | writer |
—
|
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: writer | Statement: [portraits of Lord Byron, hasDepictedPersonOccupation, writer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDepictedPersonOccupation Context triple: [portraits of Lord Byron, hasDepictedPersonOccupation, writer]
-
A.
depictsNotablePerson
Indicates that one entity visually represents or portrays a person who is considered notable or significant.
-
B.
depictsPersonRole
chosen
Indicates that an image or representation shows a person in a specific role, function, or capacity.
-
C.
hasNotablePortrayerOccupation
Indicates that the occupation specified is a notable profession of a person who portrays the given entity (such as an actor playing a character).
-
D.
hasBiographicalSubjectOccupation
Indicates that the biographical subject is or was engaged in the specified occupation or profession.
-
E.
hasPortrayedPersonRole
Indicates that an entity has performed or held a specific role in portraying a particular person (e.g., in a film, play, or other representation).
- 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_69ee883c851881909e2ab04efbb3c5fe |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f707f7959881908f037f0d6b1d0c36 |
completed | May 3, 2026, 8:31 a.m. |
| PD | Predicate disambiguation | batch_69f700fc274c8190a128593dc7c7abd0 |
completed | May 3, 2026, 8:02 a.m. |
Created at: April 26, 2026, 11:56 p.m.