Triple
T1039753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academia Brasileira de Letras |
E22443
|
entity |
| Predicate | hasChairCount |
P24255
|
FINISHED |
| Object | 40 |
—
|
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: 40 | Statement: [Academia Brasileira de Letras, hasChairCount, 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChairCount Context triple: [Academia Brasileira de Letras, hasChairCount, 40]
-
A.
hasChambersFor
Indicates that one entity contains or provides designated chambers or compartments intended for use by another entity.
-
B.
seatingCapacity
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
-
C.
plenaryChamberCapacity
Indicates the seating capacity of the main plenary chamber where formal sessions or assemblies are held.
-
D.
coChairWith
Indicates that two or more entities share the role of chairing the same group, event, or organization jointly.
-
E.
hasNumberOfCouncillors
Indicates the relationship that specifies how many councillors are associated with a given 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_69a493d91478819094cc01fb65564bc1 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b97c64a88190bf1119fdd4940bf3 |
completed | March 1, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69a4b729f8488190b2042bd9c625a833 |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b97acbf4819087b92a8b29baef46 |
completed | March 1, 2026, 10:11 p.m. |
Created at: March 1, 2026, 7:41 p.m.