Triple
T13084916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rådhuset |
E310305
|
entity |
| Predicate | stationCode |
P1289
|
FINISHED |
| Object |
RÅH
RÅH is the station code for Rådhuset, a Stockholm metro station on the blue line located near the City Hall on Kungsholmen.
|
E1021095
|
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: RÅH | Statement: [Rådhuset, stationCode, RÅH]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RÅH Context triple: [Rådhuset, stationCode, RÅH]
-
A.
RAH
RAH is the abbreviation for the Real Academia de la Historia, Spain’s national institution dedicated to the research, preservation, and promotion of the country’s historical heritage.
-
B.
ROAH
ROAH is the ICAO airport code for Naha Airport, the primary air gateway to Okinawa, Japan.
-
C.
RAKh
RAKh is the abbreviated name of the Russian Academy of Arts, a major state institution overseeing and promoting the visual arts in Russia.
-
D.
Rugah Rahj
Rugah Rahj is a music producer best known for his work on Teyana Taylor’s R&B track "Gonna Love Me."
-
E.
Rageh
Rageh is a Somali-born British journalist and television news presenter known for his international reporting and documentary work.
- 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: RÅH Triple: [Rådhuset, stationCode, RÅH]
Generated description
RÅH is the station code for Rådhuset, a Stockholm metro station on the blue line located near the City Hall on Kungsholmen.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: RÅH Target entity description: RÅH is the station code for Rådhuset, a Stockholm metro station on the blue line located near the City Hall on Kungsholmen.
-
A.
RAH
RAH is the abbreviation for the Real Academia de la Historia, Spain’s national institution dedicated to the research, preservation, and promotion of the country’s historical heritage.
-
B.
ROAH
ROAH is the ICAO airport code for Naha Airport, the primary air gateway to Okinawa, Japan.
-
C.
RAKh
RAKh is the abbreviated name of the Russian Academy of Arts, a major state institution overseeing and promoting the visual arts in Russia.
-
D.
Rugah Rahj
Rugah Rahj is a music producer best known for his work on Teyana Taylor’s R&B track "Gonna Love Me."
-
E.
Rageh
Rageh is a Somali-born British journalist and television news presenter known for his international reporting and documentary work.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d981361e8c819099376435aa3a7aa3 |
completed | April 10, 2026, 11:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6d61060188190911eb3e135dc25ac |
completed | May 3, 2026, 4:58 a.m. |
| NEDg | Description generation | batch_69f6dae595908190b27980e48514cda5 |
completed | May 3, 2026, 5:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6db8f68a4819091d8e67d9c8eec81 |
completed | May 3, 2026, 5:22 a.m. |
Created at: April 9, 2026, 9:02 p.m.