Triple
T9902162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gauda |
E182310
|
entity |
| Predicate | overlapsWith |
P1867
|
FINISHED |
| Object | Vanga |
E177619
|
NE 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: Vanga | Statement: [Gauda, overlapsWith, Vanga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vanga Context triple: [Gauda, overlapsWith, Vanga]
-
A.
Vanga
chosen
Vanga was an ancient historical region in eastern Bengal, centered in what is now southern Bangladesh and parts of the Indian state of West Bengal.
-
B.
Viliya
Viliya is an alternative name for the Neris River, a major river flowing through Belarus and Lithuania and a key tributary of the Neman.
-
C.
Vrakuňa
Vrakuňa is a borough of Bratislava, Slovakia, located in the eastern part of the city.
-
D.
Vereya
Vereya is a small historic town in Russia that was once part of the former Moscow Governorate.
-
E.
Velda
Velda is the loyal and resourceful secretary and love interest of private investigator Mike Hammer in the hardboiled crime novel and film "Kiss Me Deadly."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca82876f8081909cf75df0f99bb13f |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb4e391888190a3f5e5a1bf1cf9ff |
completed | April 2, 2026, 12:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1eb2778bc81909d7e09da718d9afa |
completed | April 5, 2026, 4:55 a.m. |
Created at: March 30, 2026, 8:40 p.m.