Triple
T28298471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adriana Lecouvreur (Adriana) |
E713634
|
entity |
| Predicate | actCountInOpera |
P164243
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Adriana Lecouvreur (Adriana), actCountInOpera, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: actCountInOpera Context triple: [Adriana Lecouvreur (Adriana), actCountInOpera, 4]
-
A.
operaAct
Indicates that an entity performs in or takes part in an act (segment) of an opera performance.
-
B.
numberOfOperas
Indicates the total count of operas associated with a given entity (such as a person, organization, or catalog entry).
-
C.
associatedOpera
Indicates that there is a relationship linking an entity to an opera with which it is connected or related (e.g., as subject, inspiration, or context).
-
D.
estimatedNumberOfOperas
Indicates the approximate count of operas associated with an entity, rather than an exact, verified number.
-
E.
operaActRole
Indicates the role or character that a performer portrays in a specific act of an opera.
- 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_69efb524ab688190a1ce7ee7c9520932 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f644b13498819095df64be37e48ae0 |
completed | May 2, 2026, 6:38 p.m. |
| PD | Predicate disambiguation | batch_69f641e0fde08190bf06a1c5b388aa84 |
completed | May 2, 2026, 6:26 p.m. |
| PDg | Predicate description generation | batch_69f6430975b481909191219ad13ef77e |
completed | May 2, 2026, 6:31 p.m. |
Created at: April 27, 2026, 11:33 p.m.