Triple
T7880707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tuva or Bust! |
E182970
|
entity |
| Predicate | mainSubject |
P3
|
FINISHED |
| Object | Tuva |
E306835
|
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: Tuva | Statement: [Tuva or Bust!, mainSubject, Tuva]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tuva Context triple: [Tuva or Bust!, mainSubject, Tuva]
-
A.
Tuva Republic
chosen
The Tuva Republic is a remote, mountainous federal subject of Russia in southern Siberia, known for its nomadic traditions, Tuvan throat singing, and location at the geographic center of Asia.
-
B.
Touqan
Touqan is a family name of notable Palestinian origin, associated with prominent poets, politicians, and intellectuals in the Arab world.
-
C.
Börjigin
Börjigin is the royal clan of Genghis Khan and his descendants that formed the ruling dynasty of the Mongol Empire.
-
D.
Terra do Sal
Terra do Sal is a nickname for Mossoró, a city in Brazil’s Rio Grande do Norte state known for its significant salt production.
-
E.
Terra da Garoa
Terra da Garoa is a popular nickname for the Brazilian metropolis of São Paulo, alluding to its characteristic light, misty rain.
- 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_69ca828a17248190b46defe758bc5ad3 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb39bffbc481908e868ab98a38e10a |
completed | March 31, 2026, 3:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5b86c20081909aa029cda7c48d44 |
completed | March 31, 2026, 5:28 a.m. |
Created at: March 30, 2026, 4:58 p.m.