Triple
T9975698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hudiksvall Municipality |
E196321
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object |
Iggesund
Iggesund is a small industrial locality in Gävleborg County, Sweden, known for its paperboard mill and proximity to the Gulf of Bothnia.
|
E834047
|
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: Iggesund | Statement: [Hudiksvall Municipality, containsSettlement, Iggesund]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Iggesund Context triple: [Hudiksvall Municipality, containsSettlement, Iggesund]
-
A.
Ginnerup
Ginnerup is a small village in Denmark best known as the birthplace of former Danish Prime Minister and NATO Secretary General Anders Fogh Rasmussen.
-
B.
Solvang
Solvang is a Danish-themed tourist town in California known for its Scandinavian architecture, bakeries, and wineries.
-
C.
Nørholm
Nørholm is a historic estate in southern Norway best known as the longtime home of Nobel Prize–winning author Knut Hamsun (born Knut Pedersen).
-
D.
Farsund
Farsund is a coastal town and municipality in southern Norway known for its maritime heritage, beaches, and historic wooden architecture.
-
E.
Sollefteå
Sollefteå is a small town in northern Sweden known for its scenic location along the Ångerman River and its surrounding forests and hills.
- 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: Iggesund Triple: [Hudiksvall Municipality, containsSettlement, Iggesund]
Generated description
Iggesund is a small industrial locality in Gävleborg County, Sweden, known for its paperboard mill and proximity to the Gulf of Bothnia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Iggesund Target entity description: Iggesund is a small industrial locality in Gävleborg County, Sweden, known for its paperboard mill and proximity to the Gulf of Bothnia.
-
A.
Ginnerup
Ginnerup is a small village in Denmark best known as the birthplace of former Danish Prime Minister and NATO Secretary General Anders Fogh Rasmussen.
-
B.
Solvang
Solvang is a Danish-themed tourist town in California known for its Scandinavian architecture, bakeries, and wineries.
-
C.
Nørholm
Nørholm is a historic estate in southern Norway best known as the longtime home of Nobel Prize–winning author Knut Hamsun (born Knut Pedersen).
-
D.
Farsund
Farsund is a coastal town and municipality in southern Norway known for its maritime heritage, beaches, and historic wooden architecture.
-
E.
Sollefteå
Sollefteå is a small town in northern Sweden known for its scenic location along the Ångerman River and its surrounding forests and hills.
- 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_69ca82eea2b88190a0e511d21a31f386 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb84b47308190aa2f94fa7320cdc3 |
completed | April 2, 2026, 12:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d257c9f6cc81908256dc1e8d6c3fea |
completed | April 5, 2026, 12:38 p.m. |
| NEDg | Description generation | batch_69d258b6d9c0819096dbd3e85dc6adfc |
completed | April 5, 2026, 12:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d25981db3c8190b7b4935cdcc1fe1c |
completed | April 5, 2026, 12:45 p.m. |
Created at: March 30, 2026, 8:48 p.m.