Triple
T1029783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kabardino-Balkaria |
E22222
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Tyrnyauz
Tyrnyauz is a mountainous town in southwestern Russia known for its former tungsten-molybdenum mining industry and location in the North Caucasus.
|
E126463
|
NE FINISHED |
How this triple was built (4 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: Tyrnyauz | Statement: [Kabardino-Balkaria, hasCity, Tyrnyauz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tyrnyauz Context triple: [Kabardino-Balkaria, hasCity, Tyrnyauz]
-
A.
Gori
Gori is a city in central Georgia best known as the birthplace of Soviet leader Joseph Stalin.
-
B.
Mytishchi
Mytishchi is a city in western Russia that serves as a major suburban and industrial center just northeast of Moscow.
-
C.
Khashuri
Khashuri is a town in central Georgia that serves as an important regional transport hub and gateway between eastern and western parts of the country.
-
D.
Tsitska
Tsitska is a Georgian white grape variety from the Imereti region, known for producing fresh, high-acidity wines often used in both still and sparkling styles.
-
E.
Yunaska
Yunaska is the maiden surname of Lara Trump, who is married to Eric Trump, son of former U.S. President Donald Trump.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tyrnyauz Triple: [Kabardino-Balkaria, hasCity, Tyrnyauz]
Generated description
Tyrnyauz is a mountainous town in southwestern Russia known for its former tungsten-molybdenum mining industry and location in the North Caucasus.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tyrnyauz Target entity description: Tyrnyauz is a mountainous town in southwestern Russia known for its former tungsten-molybdenum mining industry and location in the North Caucasus.
-
A.
Gori
Gori is a city in central Georgia best known as the birthplace of Soviet leader Joseph Stalin.
-
B.
Mytishchi
Mytishchi is a city in western Russia that serves as a major suburban and industrial center just northeast of Moscow.
-
C.
Khashuri
Khashuri is a town in central Georgia that serves as an important regional transport hub and gateway between eastern and western parts of the country.
-
D.
Tsitska
Tsitska is a Georgian white grape variety from the Imereti region, known for producing fresh, high-acidity wines often used in both still and sparkling styles.
-
E.
Yunaska
Yunaska is the maiden surname of Lara Trump, who is married to Eric Trump, son of former U.S. President Donald Trump.
- F. None of above. chosen
Provenance (5 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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b7f962608190b3ebd9140472b979 |
completed | March 1, 2026, 10:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac4c15bb8481909ba68f5807581b18 |
completed | March 7, 2026, 4:02 p.m. |
| NEDg | Description generation | batch_69ac4d3d095881908276f22c93efe52b |
completed | March 7, 2026, 4:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac4daf3f08819085dba18f97b09961 |
completed | March 7, 2026, 4:09 p.m. |
Created at: March 1, 2026, 7:41 p.m.