Triple
T14732621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iserlohn |
E346114
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Tarnowskie Góry |
E722328
|
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: Tarnowskie Góry | Statement: [Iserlohn, hasTwinTown, Tarnowskie Góry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tarnowskie Góry Context triple: [Iserlohn, hasTwinTown, Tarnowskie Góry]
-
A.
Tarnowskie Góry
chosen
Tarnowskie Góry is a historic town in southern Poland renowned for its UNESCO-listed silver, lead, and zinc mining heritage.
-
B.
Mszana Dolna
Mszana Dolna is a small town in southern Poland situated in a picturesque valley surrounded by the mountains of the Western Beskids.
-
C.
Muszyna
Muszyna is a spa and tourist town in southern Poland, known for its mineral springs and scenic mountain surroundings near the Slovak border.
-
D.
Trzebnica
Trzebnica is a historic town in southwestern Poland known for its medieval Cistercian abbey and religious significance.
-
E.
Skarżysko-Kamienna
Skarżysko-Kamienna is a town in south-central Poland known for its industrial heritage and location in the Świętokrzyskie Voivodeship.
- 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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec72ea9348190817efcdaa973d7f7 |
completed | April 14, 2026, 11:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0180bd1e5c8190a6a96581ce8a37de |
completed | May 11, 2026, 7:09 a.m. |
Created at: April 10, 2026, 1:29 a.m.