Triple
T13100138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olivier Award for Best Actress in a Play |
E310696
|
entity |
| Predicate | eligibleTypeOfWork |
P86785
|
FINISHED |
| Object | play |
—
|
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: play | Statement: [Olivier Award for Best Actress in a Play, eligibleTypeOfWork, play]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eligibleTypeOfWork Context triple: [Olivier Award for Best Actress in a Play, eligibleTypeOfWork, play]
-
A.
eligibleWork
Indicates that a particular work satisfies the necessary conditions or criteria to qualify for a specified status, benefit, or consideration.
-
B.
eligibleWorks
Indicates that certain works meet the required criteria or conditions to qualify for a specified status, use, or consideration.
-
C.
associatedWorkType
chosen
Indicates the type or category of work with which an entity is associated (e.g., publication, artwork, performance).
-
D.
employmentType
Indicates the specific kind or category of employment relationship that exists between an individual and an employer (e.g., full-time, part-time, contract).
-
E.
typeOfWork
Indicates the kind or category of work associated with or performed by an entity.
- 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_69d806a872d08190a329806f8ff30df4 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d981500d34819097037b3c3c33627b |
completed | April 10, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69d98041a3548190a05ddd83dbb660fa |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:04 p.m.