Triple
T17404103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Panevėžys County |
E423168
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Panevėžys |
—
|
NE NERFINISHED |
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: Panevėžys | Statement: [Panevėžys County, capital, Panevėžys]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Panevėžys Context triple: [Panevėžys County, capital, Panevėžys]
-
A.
Panevėžys
chosen
Panevėžys is a major city in northern Lithuania known as an important regional industrial and cultural center.
-
B.
Tauragė
Tauragė is a town in western Lithuania known as an administrative, cultural, and economic center of the surrounding region.
-
C.
Vilkaviškis
Vilkaviškis is a town in southwestern Lithuania known as an administrative and historical center of the surrounding agricultural region.
-
D.
Kaunas
Kaunas is the second-largest city in Lithuania, known as a historic cultural and academic center located at the confluence of the Nemunas and Neris rivers.
-
E.
Zarasai
Zarasai is a small town in northeastern Lithuania known for its lakes and scenic natural surroundings.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889d7d27c819088486ce3f0627fa1 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43b068248819088871d79f8a38f30 |
completed | April 19, 2026, 2:16 a.m. |
Created at: April 10, 2026, 5:45 a.m.