Triple
T13344496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Piasts of Legnica |
E317913
|
entity |
| Predicate | territoryIncluded |
P11790
|
FINISHED |
| Object |
Złotoryja
Złotoryja is a historic town in southwestern Poland, known as one of the country’s oldest gold-mining centers and an important medieval settlement in Silesia.
|
E1414000
|
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: Złotoryja | Statement: [Piasts of Legnica, territoryIncluded, Złotoryja]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Złotoryja Context triple: [Piasts of Legnica, territoryIncluded, Złotoryja]
-
A.
Kalisz
Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
-
B.
Tychy
Tychy is a city in the Silesian region of southern Poland, known for its brewing industry and role as a planned industrial center.
-
C.
Zabrze
Zabrze is an industrial city in the Silesian region of southern Poland, historically known for coal mining and heavy industry.
-
D.
Wolsztyn
Wolsztyn is a town in western Poland known for its historic steam locomotive depot and annual steam engine parade.
-
E.
Chorzów
Chorzów is an industrial city in southern Poland’s Silesian region, known for its heavy industry heritage and the extensive Silesian Park.
- 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: Złotoryja Triple: [Piasts of Legnica, territoryIncluded, Złotoryja]
Generated description
Złotoryja is a historic town in southwestern Poland, known as one of the country’s oldest gold-mining centers and an important medieval settlement in Silesia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Złotoryja Target entity description: Złotoryja is a historic town in southwestern Poland, known as one of the country’s oldest gold-mining centers and an important medieval settlement in Silesia.
-
A.
Kalisz
Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
-
B.
Tychy
Tychy is a city in the Silesian region of southern Poland, known for its brewing industry and role as a planned industrial center.
-
C.
Zabrze
Zabrze is an industrial city in the Silesian region of southern Poland, historically known for coal mining and heavy industry.
-
D.
Wolsztyn
Wolsztyn is a town in western Poland known for its historic steam locomotive depot and annual steam engine parade.
-
E.
Chorzów
Chorzów is an industrial city in southern Poland’s Silesian region, known for its heavy industry heritage and the extensive Silesian Park.
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99e8839b48190b164414b418e756c |
completed | April 11, 2026, 1:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08345238448190a0b44774008b38d0 |
completed | May 16, 2026, 9:09 a.m. |
| NEDg | Description generation | batch_6a0834ed79488190aca4ec32a3c0763e |
completed | May 16, 2026, 9:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08358d52a08190b4361485744c774c |
completed | May 16, 2026, 9:14 a.m. |
Created at: April 9, 2026, 9:31 p.m.