Triple
T343941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Honorable |
E6896
|
entity |
| Predicate | countrySpecificUsage |
P7827
|
FINISHED |
| Object | In the United States it is commonly used for federal and state judges |
—
|
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: In the United States it is commonly used for federal and state judges | Statement: [The Honorable, countrySpecificUsage, In the United States it is commonly used for federal and state judges]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countrySpecificUsage Context triple: [The Honorable, countrySpecificUsage, In the United States it is commonly used for federal and state judges]
-
A.
usedInCountry
Indicates that something is utilized, applied, or in operation within the specified country.
-
B.
usedInRegion
Indicates that something is utilized or applied within a specific geographic or administrative region.
-
C.
commonInCountry
chosen
Indicates that something occurs frequently or is widespread within a specified country.
-
D.
countryTargeted
Indicates that a particular country is the intended object or focus of an action, operation, or influence.
-
E.
usedUniformlyAcrossCountry
Indicates that something is applied or practiced in the same way throughout the entire country without regional variation.
- 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_69a2e7951ba08190960e90823b5078f3 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eb0019088190a9b969c4287dc4fa |
completed | Feb. 28, 2026, 1:17 p.m. |
| PD | Predicate disambiguation | batch_69a2e9530c98819085025efe4e04aa7e |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.