Triple
T24028159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Văn |
E595022
|
entity |
| Predicate | nameOrderInVietnamese |
P154571
|
FINISHED |
| Object | placed before given name |
—
|
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: placed before given name | Statement: [Văn, nameOrderInVietnamese, placed before given name]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nameOrderInVietnamese Context triple: [Văn, nameOrderInVietnamese, placed before given name]
-
A.
nameInVietnamese
Indicates that one entity is the Vietnamese-language name or designation of another entity.
-
B.
nameOrderInChinese
Indicates that the entities are arranged in the order that personal names are written or spoken in Chinese (family name first, given name second).
-
C.
nameOrderInJapan
Indicates that the person’s name is written or presented in the Japanese order, with the family name appearing before the given name.
-
D.
spouseNameInVietnamese
Indicates that the predicate specifies the name of a person's spouse as written or expressed in the Vietnamese language.
-
E.
hasVietnameseReading
Indicates that an entity is associated with a specific reading or pronunciation in the Vietnamese language.
- F. None of above. chosen
Provenance (4 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_69e288bf45f08190a1b6ed8cd0b9e86b |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d76d393c8190ae9b1b9e43fe0efa |
completed | April 29, 2026, 10:03 a.m. |
| PD | Predicate disambiguation | batch_69f1764345388190a3102b62ddb729b4 |
completed | April 29, 2026, 3:08 a.m. |
| PDg | Predicate description generation | batch_69f1785afe3c81909be28986ffe944bf |
completed | April 29, 2026, 3:17 a.m. |
Created at: April 17, 2026, 9:54 p.m.