Triple
T33075937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dakin Matthews |
E846360
|
entity |
| Predicate | hasWorkedInRegionalTheatre |
P195406
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Dakin Matthews, hasWorkedInRegionalTheatre, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWorkedInRegionalTheatre Context triple: [Dakin Matthews, hasWorkedInRegionalTheatre, true]
-
A.
hasBroadwayExperience
Indicates that an entity has participated in or worked on a Broadway production in some professional capacity.
-
B.
hadPreBroadwayProductionType
Indicates that an artistic work had a specific type or category of production run prior to its official Broadway opening.
-
C.
hasTheatreDistrictRole
Indicates that an entity holds a specific role, function, or designation within a theatre district.
-
D.
hasMunicipalTheatre
Indicates that a place or administrative entity possesses or contains a municipal theatre as one of its facilities.
-
E.
servedInTheatre
Indicates that an individual performed military or service duties within a specific theater of operations or geographic area during a conflict or campaign.
- 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_69f3495405b88190967af2157b43b896 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fdd07a34c08190982b8c61c2775cf6 |
completed | May 8, 2026, noon |
| PD | Predicate disambiguation | batch_69fdbd25c7908190b72fca8de7ce503f |
completed | May 8, 2026, 10:38 a.m. |
| PDg | Predicate description generation | batch_69fdd07724f88190a33ec602642d2ea3 |
completed | May 8, 2026, noon |
Created at: May 1, 2026, 1:25 a.m.