Triple
T313184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Concerto Barocco |
E7648
|
entity |
| Predicate | typicalCasting |
P11865
|
FINISHED |
| Object | two principal women |
—
|
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: two principal women | Statement: [Concerto Barocco, typicalCasting, two principal women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCasting Context triple: [Concerto Barocco, typicalCasting, two principal women]
-
A.
castIn
Indicates that an actor or performer appears in a particular film, show, or production.
-
B.
typicalVariety
Indicates that one entity is a representative or characteristic example of the variety or type defined by another entity.
-
C.
typicalSpeaker
Indicates that the subject is a prototypical or characteristic speaker or source of utterances in the context of the object.
-
D.
starredActor
Indicates that an actor performed a leading or significant role in a particular production or work.
-
E.
typicalProductionType
Indicates the usual or characteristic type of production activity associated with an entity.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea4bd6a081909bdb57602c7093b4 |
completed | Feb. 28, 2026, 1:14 p.m. |
| PD | Predicate disambiguation | batch_69a2e9428098819089d5950cd2c96dc4 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea08878c8190a5e8a90f620a3888 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:07 p.m.