Triple
T5140267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Transcona Enterprises |
E115927
|
entity |
| Predicate | businessRoleOfJudyGarland |
P61778
|
FINISHED |
| Object | producer |
—
|
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: producer | Statement: [Transcona Enterprises, businessRoleOfJudyGarland, producer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: businessRoleOfJudyGarland Context triple: [Transcona Enterprises, businessRoleOfJudyGarland, producer]
-
A.
MarilynMonroeRoleType
Indicates the type or category of role associated with Marilyn Monroe in a given context.
-
B.
hasGingerRogersRole
Indicates that an entity is assigned or associated with a role specifically identified as the "Ginger Rogers" role in a given context or production.
-
C.
genreRole
Indicates a relationship where an entity holds a specific functional or categorical role within a particular genre.
-
D.
originalBroadwayStar
Indicates that the subject was a member of the original Broadway cast in the specified role or production.
-
E.
partOfCreativeCareerOf
Indicates that one entity represents a work, role, or activity that forms a component or phase within another entity’s overall creative career.
- 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_69bd44459a988190a772a5c2ec6a1965 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd78d7f4d081908d59adcd86f52f1d |
completed | March 20, 2026, 4:42 p.m. |
| PD | Predicate disambiguation | batch_69bd77ae2f10819098bb8939106e1281 |
completed | March 20, 2026, 4:37 p.m. |
| PDg | Predicate description generation | batch_69bd78d6a1388190804dcf568ca92129 |
completed | March 20, 2026, 4:41 p.m. |
Created at: March 20, 2026, 1:43 p.m.