Triple
T9536963
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trial Chambers (ICTR) |
E230041
|
entity |
| Predicate | numberOfJudgesPerChamber |
P2279
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Trial Chambers (ICTR), numberOfJudgesPerChamber, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfJudgesPerChamber Context triple: [Trial Chambers (ICTR), numberOfJudgesPerChamber, 3]
-
A.
numberOfJudges
chosen
Indicates the total count of judges associated with a particular case, event, or entity.
-
B.
numberOfPermanentJudges
Indicates the total count of judges who hold permanent (non-temporary) positions within a given judicial body or court.
-
C.
numberOfCourtrooms
Indicates the total count of courtrooms associated with a given legal facility, jurisdiction, or court entity.
-
D.
lengthOfJudgeship
Indicates the duration of time that an individual serves or has served in a judicial office or judgeship.
-
E.
numberOfCircuitJudgesCreated
Indicates the total count of circuit judge positions that have been established or created.
- 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_69ca847b1b3081908f72bc932c17cc41 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd98ce884c8190a8b3c2dc7c73c2c9 |
completed | April 1, 2026, 10:14 p.m. |
| PD | Predicate disambiguation | batch_69cca56c44f88190a54a5d2a133bb07e |
completed | April 1, 2026, 4:56 a.m. |
Created at: March 30, 2026, 8:01 p.m.