Triple
T7697218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Judicial branch of Texas |
E174399
|
entity |
| Predicate | hasNumberOfCourtOfCriminalAppealsJudges |
P2279
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [Judicial branch of Texas, hasNumberOfCourtOfCriminalAppealsJudges, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfCourtOfCriminalAppealsJudges Context triple: [Judicial branch of Texas, hasNumberOfCourtOfCriminalAppealsJudges, 9]
-
A.
numberOfSupremeCourtJustices
Indicates the total count of individuals serving as justices on a specified Supreme Court.
-
B.
numberOfAssociateJustices
Indicates the total count of associate justices associated with a given judicial body or court.
-
C.
numberOfLordsJusticesOfAppeal
Indicates the quantity of individuals serving as Lords Justices of Appeal associated with a given entity or context.
-
D.
numberOfJudges
chosen
Indicates the total count of judges associated with a particular case, event, or 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_69c6995a72cc8190998e56daa6f8e453 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c70402169481909b219dc5f4a64b9b |
completed | March 27, 2026, 10:26 p.m. |
| PD | Predicate disambiguation | batch_69c70165e78c8190bf6b3c34e243cb81 |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:03 p.m.