Triple
T5729545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grünerløkka |
E126346
|
entity |
| Predicate | hasPark |
P105
|
FINISHED |
| Object |
Olaf Ryes plass
Olaf Ryes plass is a popular public square and park in the Grünerløkka district of Oslo, known for its green spaces, cafés, and vibrant local atmosphere.
|
E540473
|
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: Olaf Ryes plass | Statement: [Grünerløkka, hasPark, Olaf Ryes plass]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Olaf Ryes plass Context triple: [Grünerløkka, hasPark, Olaf Ryes plass]
-
A.
Olavtoppen
Olavtoppen is the highest mountain on the remote subantarctic Bouvet Island, a Norwegian dependency in the South Atlantic Ocean.
-
B.
Bjug Harstad
Bjug Harstad was a Norwegian-American Lutheran minister and educator best known for establishing Pacific Lutheran University in Washington State.
-
C.
Kragerø
Kragerø is a coastal town in Norway renowned for its picturesque archipelago, historic wooden buildings, and role as a popular summer holiday destination.
-
D.
Rubbestadneset
Rubbestadneset is a village in the municipality of Bømlo in Vestland county, on the western coast of Norway.
-
E.
Skøyen
Skøyen is a neighborhood in western Oslo, Norway, known as a busy residential and commercial hub with strong public transport connections.
- 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: Olaf Ryes plass Triple: [Grünerløkka, hasPark, Olaf Ryes plass]
Generated description
Olaf Ryes plass is a popular public square and park in the Grünerløkka district of Oslo, known for its green spaces, cafés, and vibrant local atmosphere.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Olaf Ryes plass Target entity description: Olaf Ryes plass is a popular public square and park in the Grünerløkka district of Oslo, known for its green spaces, cafés, and vibrant local atmosphere.
-
A.
Olavtoppen
Olavtoppen is the highest mountain on the remote subantarctic Bouvet Island, a Norwegian dependency in the South Atlantic Ocean.
-
B.
Bjug Harstad
Bjug Harstad was a Norwegian-American Lutheran minister and educator best known for establishing Pacific Lutheran University in Washington State.
-
C.
Kragerø
Kragerø is a coastal town in Norway renowned for its picturesque archipelago, historic wooden buildings, and role as a popular summer holiday destination.
-
D.
Rubbestadneset
Rubbestadneset is a village in the municipality of Bømlo in Vestland county, on the western coast of Norway.
-
E.
Skøyen
Skøyen is a neighborhood in western Oslo, Norway, known as a busy residential and commercial hub with strong public transport connections.
- 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_69c0082f723881908ce8bb13a0c0f8b7 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c025303860819093e51f176babed71 |
completed | March 22, 2026, 5:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a8cca748190b471c842fd2ce218 |
completed | March 22, 2026, 9:09 p.m. |
| NEDg | Description generation | batch_69c05b7c3bd48190ad8303bf1bb3ec6a |
completed | March 22, 2026, 9:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c05c22c31081909a9a67d99e7c728c |
completed | March 22, 2026, 9:16 p.m. |
Created at: March 22, 2026, 3:47 p.m.