Triple
T35453406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Takeshi Nakamura |
E1024699
|
entity |
| Predicate | hasPossibleRomanizationSystem |
P23170
|
FINISHED |
| Object | Hepburn romanization |
—
|
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: Hepburn romanization | Statement: [Takeshi Nakamura, hasPossibleRomanizationSystem, Hepburn romanization]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPossibleRomanizationSystem Context triple: [Takeshi Nakamura, hasPossibleRomanizationSystem, Hepburn romanization]
-
A.
hasRomanizationOf
Indicates that one entity is a romanized representation (written in the Latin alphabet) of the other entity’s original script form.
-
B.
hasRomanizationStandard
chosen
Indicates that an entity’s romanized form follows a specified romanization standard or system.
-
C.
hasHakkaRomanization
Indicates that an entity is associated with a specific representation of its name or term in Hakka Romanization.
-
D.
hasFormerRomanization
Indicates that an entity was previously written or represented using an earlier or superseded system of Romanized spelling.
-
E.
hasMacronRomanization
Indicates that an entity is associated with a Romanized form of text that uses macrons to mark long vowels.
- 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_69f76df92f108190817222e520e22268 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff59b33a38819086cc9aa19b81748b |
completed | May 9, 2026, 3:58 p.m. |
| PD | Predicate disambiguation | batch_69ff587758f88190a39c2164341dc554 |
completed | May 9, 2026, 3:53 p.m. |
Created at: May 3, 2026, 4:04 p.m.