Triple
T23122939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seejiq |
E576948
|
entity |
| Predicate | hasOfficialRomanization |
P23170
|
FINISHED |
| Object | Seediq orthography promoted by Taiwan government |
—
|
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: Seediq orthography promoted by Taiwan government | Statement: [Seejiq, hasOfficialRomanization, Seediq orthography promoted by Taiwan government]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOfficialRomanization Context triple: [Seejiq, hasOfficialRomanization, Seediq orthography promoted by Taiwan government]
-
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.
hasFormerRomanization
Indicates that an entity was previously written or represented using an earlier or superseded system of Romanized spelling.
-
D.
hasHakkaRomanization
Indicates that an entity is associated with a specific representation of its name or term in Hakka Romanization.
-
E.
romanizationVariantOf
Indicates that one written form is a different romanized representation of the same underlying word or expression as another.
- 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_69e245f6c2e881909a228fdcfeb7c7d3 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e517a0481909829a73fdf255d1c |
completed | April 29, 2026, 4:51 a.m. |
| PD | Predicate disambiguation | batch_69ef89f020588190b43393e048e7eda3 |
completed | April 27, 2026, 4:08 p.m. |
Created at: April 17, 2026, 3:59 p.m.