Triple
T32448126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhōnghuá Rénmín Gònghéguó Fùzhǔxí |
E829200
|
entity |
| Predicate | usedInRomanizationSystem |
P125986
|
FINISHED |
| Object | Hanyu Pinyin |
—
|
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: Hanyu Pinyin | Statement: [Zhōnghuá Rénmín Gònghéguó Fùzhǔxí, usedInRomanizationSystem, Hanyu Pinyin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedInRomanizationSystem Context triple: [Zhōnghuá Rénmín Gònghéguó Fùzhǔxí, usedInRomanizationSystem, Hanyu Pinyin]
-
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
Indicates that an entity’s romanized form follows a specified romanization standard or system.
-
C.
usesPhoneticSystem
Indicates that one entity employs or is based on a particular phonetic system for representing or encoding sounds.
-
D.
romanizationType
chosen
Indicates the specific system or method used to convert text from one writing system into its Roman (Latin) alphabet representation.
-
E.
hasHakkaRomanization
Indicates that an entity is associated with a specific representation of its name or term in Hakka Romanization.
- 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_69f3491d2e5c819092b1c9535beff8ec |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fde5d7d9548190880a9d95b8f0f66b |
completed | May 8, 2026, 1:32 p.m. |
| PD | Predicate disambiguation | batch_69fde4e1bf9c81909754545275eccc03 |
completed | May 8, 2026, 1:28 p.m. |
Created at: May 1, 2026, 12:56 a.m.