Triple
T35337593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Principal Chief of the Cherokee Nation |
E1020505
|
entity |
| Predicate | officeNameInLanguage |
P201266
|
FINISHED |
| Object | ᎠᏰᎵ ᎠᏍᎦᏯ ᎠᏰᎵ (Cherokee language title) |
—
|
NE NERFINISHED |
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: ᎠᏰᎵ ᎠᏍᎦᏯ ᎠᏰᎵ (Cherokee language title) | Statement: [Principal Chief of the Cherokee Nation, officeNameInLanguage, ᎠᏰᎵ ᎠᏍᎦᏯ ᎠᏰᎵ (Cherokee language title)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeNameInLanguage Context triple: [Principal Chief of the Cherokee Nation, officeNameInLanguage, ᎠᏰᎵ ᎠᏍᎦᏯ ᎠᏰᎵ (Cherokee language title)]
-
A.
officeNameInOfficialLanguage
Indicates that an office’s title or name is given specifically in the official language of the relevant jurisdiction or organization.
-
B.
officeNameInSpanish
Indicates that an entity’s office name is expressed in the Spanish language.
-
C.
officeNameInFrench
Indicates the French-language name or label used for a particular office or official position.
-
D.
officeNameInChinese
Indicates that an entity’s office name is expressed in the Chinese language.
-
E.
officeNameInPortuguese
Indicates the name or title of an office as expressed in the Portuguese language.
- 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_69f76debb4e08190be52d89b8af2392d |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ffe23081408190a121d901dbce1403 |
completed | May 10, 2026, 1:41 a.m. |
| PD | Predicate disambiguation | batch_69ffe18aed348190912a5996b2da728b |
completed | May 10, 2026, 1:38 a.m. |
| PDg | Predicate description generation | batch_69ffe22f453c81909867ee2d2047636f |
completed | May 10, 2026, 1:41 a.m. |
Created at: May 3, 2026, 4:03 p.m.