Triple
T8726163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lahti |
E207135
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Zielona Góra |
E149641
|
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: Zielona Góra | Statement: [Lahti, twinTown, Zielona Góra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zielona Góra Context triple: [Lahti, twinTown, Zielona Góra]
-
A.
Zielona Góra
chosen
Zielona Góra is a city in western Poland known for its wine-making tradition and annual wine festival.
-
B.
Jelenia Góra
Jelenia Góra is a historic city in southwestern Poland, known for its picturesque setting in the Karkonosze Mountains and its well-preserved old town architecture.
-
C.
Inowrocław
Inowrocław is a historic spa and industrial city in north-central Poland, known for its saltworks and location in the Kuyavia region.
-
D.
Mielec
Mielec is a town in southeastern Poland known for its aviation industry and manufacturing sector.
-
E.
Gorzów Wielkopolski
Gorzów Wielkopolski is a city in western Poland, known as one of the two capitals of the Lubusz Voivodeship and an important regional industrial and cultural center.
- 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_69ca835811d8819081ea00fd2a2c9a1c |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d158b0481908249610458f97306 |
completed | March 31, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f470cbce14819099d47d468ae61df7 |
completed | May 1, 2026, 9:22 a.m. |
Created at: March 30, 2026, 6:36 p.m.