Triple
T17011699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Länsimetro |
E412715
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Urheilupuisto
Urheilopuisto is a metro station in Espoo, Finland, serving the western extension of the Helsinki Metro network.
|
E1246155
|
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: Urheilupuisto | Statement: [Länsimetro, hasStation, Urheilupuisto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Urheilupuisto Context triple: [Länsimetro, hasStation, Urheilupuisto]
-
A.
Vasaparken
Vasaparken is a popular urban park in central Stockholm known for its open green spaces, playgrounds, and recreational areas.
-
B.
Töölönlahti Park
Töölönlahti Park is a central waterfront green space in Helsinki known for its walking paths, cultural landmarks, and views around Töölö Bay.
-
C.
Monbijoupark
Monbijoupark is a popular riverside park in central Berlin known for its green spaces, leisure areas, and views along the Spree near Museum Island.
-
D.
Alaunpark
Alaunpark is a popular urban green space in Dresden’s Neustadt district, known for its open lawns, recreational areas, and role as a central gathering spot for locals.
-
E.
Sibelius Park
Sibelius Park is a popular Helsinki park best known for its striking Sibelius Monument dedicated to Finnish composer Jean Sibelius.
- 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: Urheilupuisto Triple: [Länsimetro, hasStation, Urheilupuisto]
Generated description
Urheilopuisto is a metro station in Espoo, Finland, serving the western extension of the Helsinki Metro network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Urheilupuisto Target entity description: Urheilopuisto is a metro station in Espoo, Finland, serving the western extension of the Helsinki Metro network.
-
A.
Vasaparken
Vasaparken is a popular urban park in central Stockholm known for its open green spaces, playgrounds, and recreational areas.
-
B.
Töölönlahti Park
Töölönlahti Park is a central waterfront green space in Helsinki known for its walking paths, cultural landmarks, and views around Töölö Bay.
-
C.
Monbijoupark
Monbijoupark is a popular riverside park in central Berlin known for its green spaces, leisure areas, and views along the Spree near Museum Island.
-
D.
Alaunpark
Alaunpark is a popular urban green space in Dresden’s Neustadt district, known for its open lawns, recreational areas, and role as a central gathering spot for locals.
-
E.
Sibelius Park
Sibelius Park is a popular Helsinki park best known for its striking Sibelius Monument dedicated to Finnish composer Jean Sibelius.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d47cc17c819087f7bd27582bcbfa |
completed | April 18, 2026, 6:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a011b46e89c81908271eb22b535c558 |
completed | May 10, 2026, 11:56 p.m. |
| NEDg | Description generation | batch_6a011c99c3948190ac3d3d9059dbb57e |
completed | May 11, 2026, 12:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a011d4b53d081909781235ded1c2b35 |
completed | May 11, 2026, 12:05 a.m. |
Created at: April 10, 2026, 5:33 a.m.