Triple
T30447920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tổng Khởi Nghĩa Tháng Tám |
E774631
|
entity |
| Predicate | gắnVớiNhânVật |
P169772
|
FINISHED |
| Object | Hồ Chí Minh |
—
|
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: Hồ Chí Minh | Statement: [Tổng Khởi Nghĩa Tháng Tám, gắnVớiNhânVật, Hồ Chí Minh]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: gắnVớiNhânVật Context triple: [Tổng Khởi Nghĩa Tháng Tám, gắnVớiNhânVật, Hồ Chí Minh]
-
A.
associatedWithCharacterRole
Indicates that one entity has a connection or linkage to a specific character role played or held by another entity.
-
B.
fictionalCharacterAssociatedWith
Indicates that there is a notable connection or association between a fictional character and another entity, such as a work, creator, or universe.
-
C.
associatedWithFilmCharacterType
Indicates that an entity has an association or connection with a particular type or category of film character.
-
D.
associatedWithCharacterAlias
Indicates that one entity is linked to or connected with an alternative name or alias used for a particular character.
-
E.
associatedWithPersonInStory
Indicates that one entity has a connection or involvement with a specific person within the context of a story.
- 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_69f22493ef9c8190ae8c2afcb7f994c8 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f686c07ac48190b5169557e67861c9 |
completed | May 2, 2026, 11:20 p.m. |
| PD | Predicate disambiguation | batch_69f67e40af9881908de3a4aa15f70a83 |
completed | May 2, 2026, 10:44 p.m. |
| PDg | Predicate description generation | batch_69f67f7e116c819099aec724e9ef3763 |
completed | May 2, 2026, 10:49 p.m. |
Created at: April 29, 2026, 8:09 p.m.