Triple
T1454279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lê |
E31362
|
entity |
| Predicate | romanizationOf |
P2508
|
FINISHED |
| Object | Lê (Chữ Quốc Ngữ) |
—
|
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: Lê (Chữ Quốc Ngữ) | Statement: [Lê, romanizationOf, Lê (Chữ Quốc Ngữ)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: romanizationOf Context triple: [Lê, romanizationOf, Lê (Chữ Quốc Ngữ)]
-
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.
hasRomanizationStandard
Indicates that an entity’s romanized form follows a specified romanization standard or system.
-
C.
usesKatakanaFor
Indicates that one entity is written or represented using katakana script in relation to another entity.
-
D.
hasRomanizationContrast
Indicates that there is a meaningful difference between two or more romanized representations of the same original form.
-
E.
alternativeTransliteration
Indicates that one written form represents an alternative way of transliterating the same original text or name into another script or orthography.
- 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_69a499171a28819085b993a3ac78e363 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c57fead881909a47188ce7312406 |
completed | March 1, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69a4c47cdbd0819092022344a2f4ad7b |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8 p.m.