Triple
T29302765
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Criminal Courts (Spain) |
E743004
|
entity |
| Predicate | numberOfJudgesPerCourt |
P2279
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Criminal Courts (Spain), numberOfJudgesPerCourt, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfJudgesPerCourt Context triple: [Criminal Courts (Spain), numberOfJudgesPerCourt, 1]
-
A.
numberOfJudges
chosen
Indicates the total count of judges associated with a particular case, event, or entity.
-
B.
numberOfCourts
Indicates the quantity of courts associated with or present at a given entity or location.
-
C.
numberOfCircuitJudgesCreated
Indicates the total count of circuit judge positions that have been established or created.
-
D.
numberOfDistrictCourts
Indicates the total count of district courts associated with a given entity.
-
E.
numberOfPermanentJudges
Indicates the total count of judges who hold permanent (non-temporary) positions within a given judicial body or court.
- F. None of above.
Provenance (3 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_69f09123ed9881909f351f7541933f5e |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69ffe8ebe9fc8190b1934a3c4074370c |
completed | May 10, 2026, 2:09 a.m. |
| PD | Predicate disambiguation | batch_69ffe83a5de88190bed3e8bac86e8760 |
completed | May 10, 2026, 2:06 a.m. |
Created at: April 28, 2026, 1:11 p.m.