Triple
T15510657
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norwegian National Road 15 |
E368699
|
entity |
| Predicate | terminus |
P388
|
FINISHED |
| Object | Otta |
E331809
|
NE FINISHED |
How this triple was built (2 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: Otta | Statement: [Norwegian National Road 15, terminus, Otta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Otta Context triple: [Norwegian National Road 15, terminus, Otta]
-
A.
Otta
chosen
Otta is a small Norwegian town known as a regional transport hub and gateway to popular mountain and national park areas.
-
B.
Ott
Ott is a surname most famously associated with Mel Ott, a Hall of Fame Major League Baseball slugger for the New York Giants.
-
C.
Ota
Ota is a historically significant Awori town in southwestern Nigeria that has grown into a major industrial and educational hub.
-
D.
Ota
Ōta is a large ward in southern Tokyo, Japan, known for Haneda Airport, residential neighborhoods, and a mix of industrial and commercial areas.
-
E.
Ota
Ōta is a major industrial city in Japan’s northern Kantō region, known especially for its automotive manufacturing, including the headquarters and main plants of Subaru.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d85a1794cc8190b0b428716296e63e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03fd008708190a3657863eb9ac626 |
completed | April 16, 2026, 1:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d4cf35c8190aa8d2db6dd744c3f |
completed | May 9, 2026, 1:57 p.m. |
Created at: April 10, 2026, 3:55 a.m.