Triple
T31279548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kōji |
E797622
|
entity |
| Predicate | hasMacronMark |
P93847
|
FINISHED |
| Object | ō |
—
|
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: ō | Statement: [Kōji, hasMacronMark, ō]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMacronMark Context triple: [Kōji, hasMacronMark, ō]
-
A.
hasMacronRomanization
chosen
Indicates that an entity is associated with a Romanized form of text that uses macrons to mark long vowels.
-
B.
hasAccent
Indicates that an entity speaks with or possesses a particular accent or distinctive pronunciation style.
-
C.
hasMacrolanguage
Indicates that a language is part of, or grouped under, a broader macrolanguage that encompasses multiple closely related language varieties.
-
D.
usesToneMarks
Indicates that one entity applies or includes diacritical tone marks in the representation or transcription of another entity (such as text, language, or symbols).
-
E.
hasCaseMarking
Indicates that a linguistic element (such as a noun or pronoun) bears a specific grammatical case marking that signals its syntactic or semantic role in a clause.
- 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_69f224def9088190a37034eab3daf57f |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a0010e46d948190a51111b5270fade7 |
completed | May 10, 2026, 5 a.m. |
| PD | Predicate disambiguation | batch_6a001061d34c8190bfe73f3d7c061eb7 |
completed | May 10, 2026, 4:58 a.m. |
Created at: April 29, 2026, 9:13 p.m.