Triple
T8254905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cherokee syllabary |
E193047
|
entity |
| Predicate | caseTypes |
P81213
|
FINISHED |
| Object | uppercase |
—
|
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: uppercase | Statement: [Cherokee syllabary, caseTypes, uppercase]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: caseTypes Context triple: [Cherokee syllabary, caseTypes, uppercase]
-
A.
typicalCaseTypes
Indicates the kinds or categories of cases that are most commonly associated with or handled by a given entity.
-
B.
typeOfCasesHandled
Indicates the categories or kinds of cases that an entity (such as a person, organization, or system) is responsible for managing or processing.
-
C.
typeOfAppeals
Indicates the specific category or kind of appeals associated with or applied to a given case, decision, or legal action.
-
D.
hasTypeOfCase
Indicates that an entity is associated with or classified under a particular type or category of case.
-
E.
caseNumber
Indicates the unique identifying number assigned to a particular legal or administrative case.
- 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_69ca82dfad9c8190b8cd18fb89f50f40 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb78f8eccc8190b43204bf2f8defc1 |
completed | March 31, 2026, 7:34 a.m. |
| PD | Predicate disambiguation | batch_69cb36b6d5548190b665a6cce14c69f7 |
completed | March 31, 2026, 2:51 a.m. |
| PDg | Predicate description generation | batch_69cb44d1caa881909069fa925a91316b |
completed | March 31, 2026, 3:51 a.m. |
Created at: March 30, 2026, 5:48 p.m.