Triple
T37167804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | constitutional court |
E920834
|
entity |
| Predicate | judgesUsuallyHave |
P187653
|
FINISHED |
| Object | fixed term of office |
—
|
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: fixed term of office | Statement: [constitutional court, judgesUsuallyHave, fixed term of office]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: judgesUsuallyHave Context triple: [constitutional court, judgesUsuallyHave, fixed term of office]
-
A.
judgesAre
Indicates that one entity serves as a judge or evaluator of another entity.
-
B.
judgesMayBe
Indicates that certain individuals can serve in the role of judges under specified conditions or classifications.
-
C.
hasJudges
Indicates that one entity serves as a judge or panel of judges for another entity, such as an event, competition, or legal case.
-
D.
judgesTerm
Indicates that one entity formally evaluates or makes a judgment about a specific term or expression.
-
E.
numberOfJudges
Indicates the total count of judges associated with a particular case, event, or 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_69f76ea16f288190b445aa1604d996f4 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb78cbef988190b8f79d946b46e6b2 |
completed | May 6, 2026, 5:22 p.m. |
| PD | Predicate disambiguation | batch_69fb5a9ac5a08190b24ef308963fc52b |
completed | May 6, 2026, 3:13 p.m. |
| PDg | Predicate description generation | batch_69fb78c982ac8190846efe8f6209e5d1 |
completed | May 6, 2026, 5:22 p.m. |
Created at: May 3, 2026, 4:15 p.m.