Triple
T1231508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grozny |
E26451
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object |
Gudermes
Gudermes is a town in the Chechen Republic of Russia that serves as an important regional transport and administrative center.
|
E145130
|
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: Gudermes | Statement: [Grozny, nearbyCity, Gudermes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gudermes Context triple: [Grozny, nearbyCity, Gudermes]
-
A.
Pyatigorsk
Pyatigorsk is a historic spa and resort city in southern Russia, known for its mineral springs and location in the North Caucasus region.
-
B.
Khasavyurt
Khasavyurt is a significant urban center in the Republic of Dagestan, Russia, known as an important regional hub in the North Caucasus.
-
C.
Buynaksk
Buynaksk is a significant urban center in the Republic of Dagestan in southern Russia, known for its strategic location in the North Caucasus region.
-
D.
Tskaltubo
Tskaltubo is a spa town in western Georgia renowned for its radon-carbonate mineral springs and Soviet-era sanatoriums.
-
E.
Dashoguz
Dashoguz is a prominent city in northern Turkmenistan, serving as a regional administrative and economic center near the border with Uzbekistan.
- 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: Gudermes Triple: [Grozny, nearbyCity, Gudermes]
Generated description
Gudermes is a town in the Chechen Republic of Russia that serves as an important regional transport and administrative center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gudermes Target entity description: Gudermes is a town in the Chechen Republic of Russia that serves as an important regional transport and administrative center.
-
A.
Pyatigorsk
Pyatigorsk is a historic spa and resort city in southern Russia, known for its mineral springs and location in the North Caucasus region.
-
B.
Khasavyurt
Khasavyurt is a significant urban center in the Republic of Dagestan, Russia, known as an important regional hub in the North Caucasus.
-
C.
Buynaksk
Buynaksk is a significant urban center in the Republic of Dagestan in southern Russia, known for its strategic location in the North Caucasus region.
-
D.
Tskaltubo
Tskaltubo is a spa town in western Georgia renowned for its radon-carbonate mineral springs and Soviet-era sanatoriums.
-
E.
Dashoguz
Dashoguz is a prominent city in northern Turkmenistan, serving as a regional administrative and economic center near the border with Uzbekistan.
- 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_69a4948571c88190a9191e451e6035fd |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be5a25348190a0665b6324c4d8f5 |
completed | March 1, 2026, 10:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac997c6acc819099b1ae6e9ef47a8e |
completed | March 7, 2026, 9:32 p.m. |
| NEDg | Description generation | batch_69ac99ef5e308190858cf45c0707aa74 |
completed | March 7, 2026, 9:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac9ac980f48190a23da8b222ec7601 |
completed | March 7, 2026, 9:38 p.m. |
Created at: March 1, 2026, 7:47 p.m.