Triple
T16704775
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chan (surname 詹) |
E405939
|
entity |
| Predicate | canAlsoRomanizePronunciationOf |
P2508
|
FINISHED |
| Object | Cantonese reading of 占 |
—
|
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: Cantonese reading of 占 | Statement: [Chan (surname 詹), canAlsoRomanizePronunciationOf, Cantonese reading of 占]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canAlsoRomanizePronunciationOf Context triple: [Chan (surname 詹), canAlsoRomanizePronunciationOf, Cantonese reading of 占]
-
A.
hasRomanizationOf
chosen
Indicates that one entity is a romanized representation (written in the Latin alphabet) of the other entity’s original script form.
-
B.
hasMacronRomanization
Indicates that an entity is associated with a Romanized form of text that uses macrons to mark long vowels.
-
C.
hasRomanizationStandard
Indicates that an entity’s romanized form follows a specified romanization standard or system.
-
D.
hasHakkaRomanization
Indicates that an entity is associated with a specific representation of its name or term in Hakka Romanization.
-
E.
romanizesVowel
Indicates the action of converting a vowel from a non-Roman writing system into its corresponding representation in the Roman (Latin) alphabet.
- 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_69d8838db21081909589220fd71440a4 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3833496dc8190ae4b4a03ba04d69d |
completed | April 18, 2026, 1:12 p.m. |
| PD | Predicate disambiguation | batch_69e319c379f88190ac0adf812486f598 |
completed | April 18, 2026, 5:42 a.m. |
Created at: April 10, 2026, 5:19 a.m.