Triple
T6337983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tuchola Forest |
E142539
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Tuchola
Tuchola is a town in northern Poland that lends its name to the surrounding Tuchola Forest, one of the largest forest complexes in the country.
|
E705471
|
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: Tuchola | Statement: [Tuchola Forest, namedAfter, Tuchola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tuchola Context triple: [Tuchola Forest, namedAfter, Tuchola]
-
A.
Chojnice
Chojnice is a historic town in northern Poland known for its medieval architecture and role as a local cultural and economic center.
-
B.
Muszyna
Muszyna is a spa and tourist town in southern Poland, known for its mineral springs and scenic mountain surroundings near the Slovak border.
-
C.
Wiślica
Wiślica is a historic town in south-central Poland, known for its medieval architecture and archaeological significance as one of the country’s oldest settlements.
-
D.
Łęczna
Łęczna is a town in eastern Poland known for its location near the Lublin Coal Basin and as a local administrative and service center.
-
E.
Mława
Mława is a town in north-central Poland known for its historical significance, including a major World War II battle, and its regional cultural and economic role.
- 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: Tuchola Triple: [Tuchola Forest, namedAfter, Tuchola]
Generated description
Tuchola is a town in northern Poland that lends its name to the surrounding Tuchola Forest, one of the largest forest complexes in the country.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tuchola Target entity description: Tuchola is a town in northern Poland that lends its name to the surrounding Tuchola Forest, one of the largest forest complexes in the country.
-
A.
Chojnice
Chojnice is a historic town in northern Poland known for its medieval architecture and role as a local cultural and economic center.
-
B.
Muszyna
Muszyna is a spa and tourist town in southern Poland, known for its mineral springs and scenic mountain surroundings near the Slovak border.
-
C.
Wiślica
Wiślica is a historic town in south-central Poland, known for its medieval architecture and archaeological significance as one of the country’s oldest settlements.
-
D.
Łęczna
Łęczna is a town in eastern Poland known for its location near the Lublin Coal Basin and as a local administrative and service center.
-
E.
Mława
Mława is a town in north-central Poland known for its historical significance, including a major World War II battle, and its regional cultural and economic role.
- 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_69c008d4d8e88190ad301c05b08722ac |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0654e11988190b708426d3003716a |
completed | March 22, 2026, 9:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbde8e8264819082a3954072dffd09 |
completed | March 31, 2026, 2:47 p.m. |
| NEDg | Description generation | batch_69cc46bca04481908852425c214a4e34 |
completed | March 31, 2026, 10:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cc49129e188190aaebd6a1188788d9 |
completed | March 31, 2026, 10:22 p.m. |
Created at: March 22, 2026, 4:30 p.m.