Triple
T6592355
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oslo commuter rail |
E148391
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object |
Jaren
Jaren is a village and transport hub in Gran municipality in Innlandet county, Norway, serving as a stop on the rail line north of Oslo.
|
E599685
|
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: Jaren | Statement: [Oslo commuter rail, connects, Jaren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jaren Context triple: [Oslo commuter rail, connects, Jaren]
-
A.
Jahren
Jahren is a surname most notably associated with Hope Jahren, an American geochemist, geobiologist, and author known for her work on plant science and her memoir "Lab Girl."
-
B.
Yillah
Yillah is a mysterious, ethereal woman who serves as a symbolic and spiritual figure in Herman Melville’s novel "Mardi."
-
C.
Jan
Jan is a common Dutch given name, often used as a masculine form of "John" and borne by many notable figures in the Netherlands and other Dutch-speaking regions.
-
D.
Jan
Jan is an alternative romanization of the name Zhan, used to represent the same underlying name in different transliteration systems.
-
E.
Jan
Jan is a fictional character appearing in the Traveling Wilburys’ song “Tweeter and the Monkey Man,” which tells a noir-style crime story.
- 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: Jaren Triple: [Oslo commuter rail, connects, Jaren]
Generated description
Jaren is a village and transport hub in Gran municipality in Innlandet county, Norway, serving as a stop on the rail line north of Oslo.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jaren Target entity description: Jaren is a village and transport hub in Gran municipality in Innlandet county, Norway, serving as a stop on the rail line north of Oslo.
-
A.
Jahren
Jahren is a surname most notably associated with Hope Jahren, an American geochemist, geobiologist, and author known for her work on plant science and her memoir "Lab Girl."
-
B.
Yillah
Yillah is a mysterious, ethereal woman who serves as a symbolic and spiritual figure in Herman Melville’s novel "Mardi."
-
C.
Jan
Jan is a common Dutch given name, often used as a masculine form of "John" and borne by many notable figures in the Netherlands and other Dutch-speaking regions.
-
D.
Jan
Jan is the given name of the Dutch mathematician and philosopher Luitzen Egbertus Jan Brouwer, a founder of intuitionism in the foundations of mathematics.
-
E.
Jan
Jan is an alternative romanization of the name Zhan, used to represent the same underlying name in different transliteration systems.
- 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_69c687e7b8688190811ffee72e096468 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6aece1f848190a11676e072afb002 |
completed | March 27, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cbba656c81909c3876a8f2f7300e |
completed | March 27, 2026, 6:26 p.m. |
| NEDg | Description generation | batch_69c6cd08a9c88190a481d4d3f8e680bf |
completed | March 27, 2026, 6:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6cdc859cc8190bbae2efc39409021 |
completed | March 27, 2026, 6:34 p.m. |
Created at: March 27, 2026, 1:55 p.m.