Triple
T27011440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Takashi Satō |
E680396
|
entity |
| Predicate | hasDiacriticInRomanization |
P93847
|
FINISHED |
| Object | macron on o in "Satō" |
—
|
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: macron on o in "Satō" | Statement: [Takashi Satō, hasDiacriticInRomanization, macron on o in "Satō"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDiacriticInRomanization Context triple: [Takashi Satō, hasDiacriticInRomanization, macron on o in "Satō"]
-
A.
hasRomanizationOf
Indicates that one entity is a romanized representation (written in the Latin alphabet) of the other entity’s original script form.
-
B.
usesDiacritics
Indicates that the referenced text or linguistic element employs diacritical marks as part of its written form.
-
C.
hasMacronRomanization
chosen
Indicates that an entity is associated with a Romanized form of text that uses macrons to mark long vowels.
-
D.
hasHakkaRomanization
Indicates that an entity is associated with a specific representation of its name or term in Hakka Romanization.
-
E.
diacriticFunction
Indicates that a diacritic serves a particular role or effect in relation to the base character or linguistic unit it modifies.
- 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_69eeeb53939c8190bd431f32b060f01f |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f757898fe48190b124dc7301672623 |
completed | May 3, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69f754c484348190948d2a04ff228fb1 |
completed | May 3, 2026, 1:59 p.m. |
Created at: April 27, 2026, 7:03 a.m.