Triple
T19939261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MDMK |
E479259
|
entity |
| Predicate | hasKeyPerson |
P256
|
FINISHED |
| Object | Vaiko |
—
|
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: Vaiko | Statement: [MDMK, hasKeyPerson, Vaiko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vaiko Context triple: [MDMK, hasKeyPerson, Vaiko]
-
A.
Vaiko
chosen
Vaiko is an Indian politician from Tamil Nadu, best known as a fiery orator and prominent regional leader who broke away from the DMK to form his own party and has long advocated Tamil nationalist and civil rights causes.
-
B.
Nauvo
Nauvo is a Finnish island village and former municipality in the Turku Archipelago, known for its maritime heritage, summer tourism, and scenic coastal landscapes.
-
C.
Davo
Davo is a common informal nickname or short form of the given name David, often used in English-speaking countries.
-
D.
Matta
Matta is a surname most prominently associated with Thad Matta, a successful American college basketball coach known for his tenures at Xavier and Ohio State.
-
E.
Matta
Matta is a town located in Pakistan’s Swat District, known for its agricultural surroundings and scenic mountainous landscape.
- 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_69d8e522a17c819095165d4d24939fd8 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65a19d77c819088bce99c94568d0d |
completed | April 20, 2026, 4:53 p.m. |
Created at: April 10, 2026, 1:53 p.m.