Triple
T7462755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tierp Municipality |
E176291
|
entity |
| Predicate | hasLocality |
P7943
|
FINISHED |
| Object |
Örbyhus
Örbyhus is a small locality in Uppsala County, Sweden, known for its railway connections and proximity to historical sites such as Örbyhus Castle.
|
E666357
|
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: Örbyhus | Statement: [Tierp Municipality, hasLocality, Örbyhus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Örbyhus Context triple: [Tierp Municipality, hasLocality, Örbyhus]
-
A.
Hörby
Hörby is a small municipality in southern Sweden’s Skåne County, known for its rural landscape and traditional Swedish town character.
-
B.
Vårby
Vårby is a suburban district in the southern Stockholm area of Sweden, known for its residential neighborhoods and proximity to Lake Mälaren.
-
C.
Norsborg
Norsborg is a suburban district in Botkyrka Municipality, southwest of central Stockholm, Sweden, known as the terminus area of the Stockholm metro’s red line.
-
D.
Rosersberg
Rosersberg is a locality in Stockholm County, Sweden, known for its historic Rosersberg Palace and its location near Stockholm Arlanda Airport.
-
E.
Blokhus
Blokhus is a Danish seaside resort town known for its wide sandy beaches, coastal dunes, and tourism along the North Sea.
- 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: Örbyhus Triple: [Tierp Municipality, hasLocality, Örbyhus]
Generated description
Örbyhus is a small locality in Uppsala County, Sweden, known for its railway connections and proximity to historical sites such as Örbyhus Castle.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Örbyhus Target entity description: Örbyhus is a small locality in Uppsala County, Sweden, known for its railway connections and proximity to historical sites such as Örbyhus Castle.
-
A.
Hörby
Hörby is a small municipality in southern Sweden’s Skåne County, known for its rural landscape and traditional Swedish town character.
-
B.
Vårby
Vårby is a suburban district in the southern Stockholm area of Sweden, known for its residential neighborhoods and proximity to Lake Mälaren.
-
C.
Norsborg
Norsborg is a suburban district in Botkyrka Municipality, southwest of central Stockholm, Sweden, known as the terminus area of the Stockholm metro’s red line.
-
D.
Rosersberg
Rosersberg is a locality in Stockholm County, Sweden, known for its historic Rosersberg Palace and its location near Stockholm Arlanda Airport.
-
E.
Blokhus
Blokhus is a Danish seaside resort town known for its wide sandy beaches, coastal dunes, and tourism along the North Sea.
- 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_69c69f21632481908bf83f6c6da897e3 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f3d80ae08190ba383066cf0cb2ce |
completed | March 27, 2026, 9:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c83464fc48819086f206f4d6b840ab |
completed | March 28, 2026, 8:04 p.m. |
| NEDg | Description generation | batch_69c8355f75908190ae3716f9dbff27ef |
completed | March 28, 2026, 8:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c83621b32c8190bd4b289b5f9f1764 |
completed | March 28, 2026, 8:12 p.m. |
Created at: March 27, 2026, 3:39 p.m.