Triple
T34086018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Collegiate Circuit Courts of Mexico |
E874176
|
entity |
| Predicate | typeOfPanel |
P153711
|
FINISHED |
| Object | three-judge panel |
—
|
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: three-judge panel | Statement: [Collegiate Circuit Courts of Mexico, typeOfPanel, three-judge panel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfPanel Context triple: [Collegiate Circuit Courts of Mexico, typeOfPanel, three-judge panel]
-
A.
hasPanelType
chosen
Indicates that an entity is associated with or characterized by a specific type or category of panel.
-
B.
controlPanelType
Indicates the specific kind or category of control panel associated with an entity.
-
C.
hasPanel
Indicates that one entity includes, is equipped with, or is associated with a panel as a component or feature.
-
D.
typicalPanelSize
Indicates the usual or standard dimensions associated with a given panel.
-
E.
numberOfPanels
Indicates the total count of distinct panels associated with or contained within a given entity.
- 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_69f349a61d448190b74642f325d3eb7a |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a011e5a419081908d06a07b395ebd97 |
completed | May 11, 2026, 12:10 a.m. |
| PD | Predicate disambiguation | batch_6a011de119048190b27d361cffabc228 |
completed | May 11, 2026, 12:08 a.m. |
Created at: May 1, 2026, 1:52 a.m.