Triple
T9231264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Judiciary Act of 1869 |
E221820
|
entity |
| Predicate | numberOfCircuitJudgesCreated |
P87696
|
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: [Judiciary Act of 1869, numberOfCircuitJudgesCreated, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCircuitJudgesCreated Context triple: [Judiciary Act of 1869, numberOfCircuitJudgesCreated, 9]
-
A.
numberOfJudges
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.
numberOfCircuits
Indicates the total count of circuits associated with or contained in a given entity or system.
-
D.
numberOfCourts
Indicates the quantity of courts associated with or present at a given entity or location.
-
E.
lengthOfJudgeship
Indicates the duration of time that an individual serves or has served in a judicial office or judgeship.
- 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_69ca83ed628c8190bc02d641e57f097f |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccee190204819096b38270a0abc116 |
completed | April 1, 2026, 10:06 a.m. |
| PD | Predicate disambiguation | batch_69cc7a3daeb481908b0abde3fbc1f1f0 |
completed | April 1, 2026, 1:51 a.m. |
| PDg | Predicate description generation | batch_69cc95597be081908ece2491dd2f0f74 |
completed | April 1, 2026, 3:47 a.m. |
Created at: March 30, 2026, 7:29 p.m.