Triple
T22173280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viet Minh suffered heavy losses |
E547976
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Na San |
—
|
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: Na San | Statement: [Viet Minh suffered heavy losses, location, Na San]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Na San Context triple: [Viet Minh suffered heavy losses, location, Na San]
-
A.
Na San
chosen
Na San is a locality in northwestern Vietnam known primarily as the site of a major French defensive victory over the Viet Minh during the First Indochina War.
-
B.
Son La
Son La is a city in northwestern Vietnam known as a regional administrative and economic center in a mountainous area inhabited largely by ethnic minority groups.
-
C.
Sanchica
Sanchica is the fictional daughter of Sancho Panza in Miguel de Cervantes' novel "Don Quixote."
-
D.
Sana
Sana is a Japanese singer and dancer best known as a member of the South Korean girl group Twice.
-
E.
Sana
Sana is a character in Naguib Mahfouz’s novel "The Thief and the Dogs," playing a role in the protagonist’s turbulent, psychologically driven narrative.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e3d53f88190a2b690e3f25bb062 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12a69c12c8190a03177b5b740456a |
completed | April 28, 2026, 9:45 p.m. |
Created at: April 16, 2026, 8:34 p.m.