Triple
T6625840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamm |
E149797
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Kalisz |
E133882
|
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: Kalisz | Statement: [Hamm, hasTwinTown, Kalisz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kalisz Context triple: [Hamm, hasTwinTown, Kalisz]
-
A.
Kalisz
chosen
Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
-
B.
Kielce
Kielce is a city in south-central Poland known as an important regional center for industry, education, and culture.
-
C.
Tychy
Tychy is a city in the Silesian region of southern Poland, known for its brewing industry and role as a planned industrial center.
-
D.
Bielsko-Biała
Bielsko-Biała is a city in southern Poland at the foot of the Beskid Mountains, known as a regional industrial and cultural center formed from the historic towns of Bielsko and Biała.
-
E.
Tarnów
Tarnów is a historic city in southern Poland known for its well-preserved Old Town, Renaissance architecture, and cultural heritage.
- 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_69c687ee50048190aa151765bef16193 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6af8187d881908b7a86f2cae5de23 |
completed | March 27, 2026, 4:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d065a63f8c8190a50f814fb70e0e31 |
completed | April 4, 2026, 1:13 a.m. |
Created at: March 27, 2026, 1:58 p.m.