Triple
T17325677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chuy Region |
E420680
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Kemin |
E1192921
|
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: Kemin | Statement: [Chuy Region, hasMajorCity, Kemin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kemin Context triple: [Chuy Region, hasMajorCity, Kemin]
-
A.
Kemin
chosen
Kemin is a small town in northern Kyrgyzstan that serves as an administrative and economic center in the Chüy Region.
-
B.
Bunge
Bunge is the unicameral legislative body and main law-making institution of the United Republic of Tanzania.
-
C.
Bunge
Bunge is a surname most notably associated with Nikolai Bunge, a prominent 19th-century Russian economist and statesman.
-
D.
Steenbock
Steenbock is a German-origin surname most notably associated with biochemist Harry Steenbock, known for his pioneering work on vitamin D fortification.
-
E.
Calgon
Calgon is a well-known brand of water softener and cleaning products used to prevent limescale buildup in household appliances such as washing machines.
- 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_69d889d3adc881909319f1edb8d2a956 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e439d24e548190a766dd246a4d63d4 |
completed | April 19, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a018c4c2dc08190b60982abc9ac7c9c |
completed | May 11, 2026, 7:59 a.m. |
Created at: April 10, 2026, 5:43 a.m.