Triple
T16584869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gwyneth Paltrow as Maud Bailey |
E402927
|
entity |
| Predicate | characterFieldOfStudy |
P123410
|
FINISHED |
| Object | VictorianPoetry |
—
|
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: VictorianPoetry | Statement: [Gwyneth Paltrow as Maud Bailey, characterFieldOfStudy, VictorianPoetry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterFieldOfStudy Context triple: [Gwyneth Paltrow as Maud Bailey, characterFieldOfStudy, VictorianPoetry]
-
A.
hasSubjectOfStudy
Indicates that an entity (such as a person or organization) focuses on, researches, or specializes in a particular field or topic of study.
-
B.
offersFieldOfStudy
Indicates that an institution or program provides a particular field of study as an available area of academic focus.
-
C.
dimensionOfStudy
Indicates the specific field, aspect, or perspective that characterizes or structures a particular study or research activity.
-
D.
educationField
Indicates the academic or professional discipline in which an entity has been educated or trained.
-
E.
partOfStudy
Indicates that something is a component, segment, or subset within a larger study or research project.
- 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_69d88387363c8190a97a0c942130de97 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3599b057881909fcb8bbb156633a8 |
completed | April 18, 2026, 10:14 a.m. |
| PD | Predicate disambiguation | batch_69e296a7d9d0819088555bca6c936e79 |
completed | April 17, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69e2d7fb02f481908885a226c2191231 |
completed | April 18, 2026, 1:01 a.m. |
Created at: April 10, 2026, 5:16 a.m.