Triple
T13089263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheyenne orthography |
E310416
|
entity |
| Predicate | representsTone |
P67251
|
FINISHED |
| Object | not systematically |
—
|
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: not systematically | Statement: [Cheyenne orthography, representsTone, not systematically]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: representsTone Context triple: [Cheyenne orthography, representsTone, not systematically]
-
A.
representsNumberOfTones
Indicates that one entity specifies or encodes the number of tones associated with another entity.
-
B.
contributesToTone
Indicates that one entity plays a role in shaping, influencing, or determining the overall tone or mood of another entity.
-
C.
inTonality
Indicates that something (such as a musical element, passage, or piece) is expressed, structured, or interpreted within a specific musical key or tonal framework.
-
D.
marksTones
chosen
Indicates that one entity applies or denotes tonal markings or distinctions on another entity, such as in language or notation.
-
E.
hasLiturgicalTone
Indicates that something (such as a chant, text, or musical phrase) is associated with or performed in a specific liturgical tone or mode.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d98138a1d481908a139f2f67eb3472 |
completed | April 10, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69d9803f6c508190bfadfbc2d00c2c64 |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:03 p.m.