Triple
T5359993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huta Stalowa Wola |
E102995
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
Stalowa Wola
Stalowa Wola is an industrial city in southeastern Poland, historically known as a major center of heavy industry and steel production.
|
E692490
|
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: Stalowa Wola | Statement: [Huta Stalowa Wola, locatedIn, Stalowa Wola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stalowa Wola Context triple: [Huta Stalowa Wola, locatedIn, Stalowa Wola]
-
A.
Chorzów
Chorzów is an industrial city in southern Poland’s Silesian region, known for its heavy industry heritage and the extensive Silesian Park.
-
B.
Sosnowiec
Sosnowiec is an industrial city in southern Poland, located in the Silesian Voivodeship and known as part of the Upper Silesian metropolitan area.
-
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.
Kociewie
Kociewie is an ethnocultural region in northern Poland known for its distinct folk traditions, dialect, and rural landscapes.
-
E.
Polkowice
Polkowice is a town in southwestern Poland known for its copper mining industry and location within the Lower Silesian region.
- 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: Stalowa Wola Triple: [Huta Stalowa Wola, locatedIn, Stalowa Wola]
Generated description
Stalowa Wola is an industrial city in southeastern Poland, historically known as a major center of heavy industry and steel production.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stalowa Wola Target entity description: Stalowa Wola is an industrial city in southeastern Poland, historically known as a major center of heavy industry and steel production.
-
A.
Chorzów
Chorzów is an industrial city in southern Poland’s Silesian region, known for its heavy industry heritage and the extensive Silesian Park.
-
B.
Sosnowiec
Sosnowiec is an industrial city in southern Poland, located in the Silesian Voivodeship and known as part of the Upper Silesian metropolitan area.
-
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.
Kociewie
Kociewie is an ethnocultural region in northern Poland known for its distinct folk traditions, dialect, and rural landscapes.
-
E.
Polkowice
Polkowice is a town in southwestern Poland known for its copper mining industry and location within the Lower Silesian region.
- 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_69bd43daa3e4819090b59d127db70e57 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd86330e4c8190b5452226886287b3 |
completed | March 20, 2026, 5:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ca47023570819082904801962f8dcb |
completed | March 30, 2026, 9:48 a.m. |
| NEDg | Description generation | batch_69ca47972d688190ac7a81dab08fde11 |
completed | March 30, 2026, 9:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ca483184708190a30d53a0041db6a7 |
completed | March 30, 2026, 9:53 a.m. |
Created at: March 20, 2026, 2:02 p.m.