Triple
T5078002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kingdom of Armenia |
E114445
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Armavir |
E204437
|
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: Armavir | Statement: [Kingdom of Armenia, capital, Armavir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Armavir Context triple: [Kingdom of Armenia, capital, Armavir]
-
A.
Armavir
Armavir is a city in southwestern Russia known as an industrial and transportation center on the Kuban River.
-
B.
Armavir Province
chosen
Armavir Province is a region in western Armenia known as a historical and religious center, notably home to the spiritual heart of the Armenian Apostolic Church.
-
C.
Ekaterinodar
Ekaterinodar, now known as Krasnodar, was a major city in southern Russia that served as an important political and military center in the Kuban region.
-
D.
Kislovodsk
Kislovodsk is a Russian spa and resort city in the North Caucasus, renowned for its mineral springs and mountainous surroundings.
-
E.
Gelendzhik
Gelendzhik is a Black Sea resort city in southern Russia known for its beaches, scenic bay, and tourism infrastructure.
- 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_69bd443dbf908190a9401e9c2dc7bd7d |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd74f632788190ac4fd047e1a20485 |
completed | March 20, 2026, 4:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beb12718388190974df282ec2c6a11 |
completed | March 21, 2026, 2:54 p.m. |
Created at: March 20, 2026, 1:39 p.m.