Triple
T2478902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hans Christian Andersen (screenplay contributor) |
E55157
|
entity |
| Predicate | occupationOfAuthor |
P14167
|
FINISHED |
| Object | playwright |
—
|
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: playwright | Statement: [Hans Christian Andersen (screenplay contributor), occupationOfAuthor, playwright]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occupationOfAuthor Context triple: [Hans Christian Andersen (screenplay contributor), occupationOfAuthor, playwright]
-
A.
authorOccupation
Indicates the professional role or job that an author holds or is associated with.
-
B.
workInAuthorCareer
Indicates that an author’s professional work or role occurs within and is part of their overall writing career.
-
C.
creatorOccupation
Indicates the professional role or job that the creator of an entity holds or held.
-
D.
occupationOf
chosen
Indicates that one entity holds or performs the job, role, or profession associated with another entity.
-
E.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
- 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_69ab49e279e88190ab10d7248aea9d11 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd1eb3be481908fa7c6b8f1c78209 |
completed | March 7, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69abd0b5e3d481909a5cbc4a96edd24f |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:45 p.m.