Triple
T11746673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aukštaitija |
E279296
|
entity |
| Predicate | majorCity |
P316
|
FINISHED |
| Object |
Visaginas
Visaginas is a town in northeastern Lithuania known for its Soviet-era origins and proximity to the now-decommissioned Ignalina Nuclear Power Plant.
|
E952748
|
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: Visaginas | Statement: [Aukštaitija, majorCity, Visaginas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Visaginas Context triple: [Aukštaitija, majorCity, Visaginas]
-
A.
Varniai
Varniai is a historic town in Lithuania that once served as a key political and religious center of the Samogitian region.
-
B.
Kupiškis
Kupiškis is a small town in northeastern Lithuania known for its historical architecture and location within the ethnographic region of Aukštaitija.
-
C.
Vilkaviškis
Vilkaviškis is a town in southwestern Lithuania known as an administrative and historical center of the surrounding agricultural region.
-
D.
Švenčionys
Švenčionys is a small historic town in eastern Lithuania known for its multicultural past and former Jewish community.
-
E.
Zarasai
Zarasai is a small town in northeastern Lithuania known for its lakes and scenic natural surroundings.
- 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: Visaginas Triple: [Aukštaitija, majorCity, Visaginas]
Generated description
Visaginas is a town in northeastern Lithuania known for its Soviet-era origins and proximity to the now-decommissioned Ignalina Nuclear Power Plant.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Visaginas Target entity description: Visaginas is a town in northeastern Lithuania known for its Soviet-era origins and proximity to the now-decommissioned Ignalina Nuclear Power Plant.
-
A.
Varniai
Varniai is a historic town in Lithuania that once served as a key political and religious center of the Samogitian region.
-
B.
Kupiškis
Kupiškis is a small town in northeastern Lithuania known for its historical architecture and location within the ethnographic region of Aukštaitija.
-
C.
Vilkaviškis
Vilkaviškis is a town in southwestern Lithuania known as an administrative and historical center of the surrounding agricultural region.
-
D.
Švenčionys
Švenčionys is a small historic town in eastern Lithuania known for its multicultural past and former Jewish community.
-
E.
Zarasai
Zarasai is a small town in northeastern Lithuania known for its lakes and scenic natural surroundings.
- 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_69d6ab01038c819080714901502c84fc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a50763a081908597da118bd0a64e |
completed | April 10, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f4174972ac819094f3938b18a5081e |
completed | May 1, 2026, 3 a.m. |
| NEDg | Description generation | batch_69f41f16f43c81909f5d36e8b4b0b9c3 |
completed | May 1, 2026, 3:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f4225a4b5c8190958aaddbd10035b1 |
completed | May 1, 2026, 3:47 a.m. |
Created at: April 8, 2026, 9:41 p.m.