Triple
T21195038
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Labor Courts of Brazil |
E522304
|
entity |
| Predicate | numberOfRegionalCourts |
P143509
|
FINISHED |
| Object | 24 |
—
|
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: 24 | Statement: [Labor Courts of Brazil, numberOfRegionalCourts, 24]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRegionalCourts Context triple: [Labor Courts of Brazil, numberOfRegionalCourts, 24]
-
A.
numberOfCourts
Indicates the quantity of courts associated with or present at a given entity or location.
-
B.
numberOfJurisdictions
Indicates the count of distinct legal or administrative jurisdictions associated with or applicable to an entity or situation.
-
C.
regionalCourt
Indicates that a court operates at a regional level within a larger judicial or administrative system.
-
D.
numberOfCourtrooms
Indicates the total count of courtrooms associated with a given legal facility, jurisdiction, or court entity.
-
E.
hasNumberOfJudicialCircuits
Indicates the specific count of judicial circuits 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_69e0b51061388190aa03f19700d3ef04 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7333aa0fc81909b17eb6a26f389ec |
completed | April 21, 2026, 8:20 a.m. |
| PD | Predicate disambiguation | batch_69e5f6094e3c81909ee9699e00d371f7 |
completed | April 20, 2026, 9:46 a.m. |
| PDg | Predicate description generation | batch_69e5fa92a2448190896c022dd27511ad |
completed | April 20, 2026, 10:06 a.m. |
Created at: April 16, 2026, 3:08 p.m.