Triple
T1512857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gulf of Bothnia |
E32052
|
entity |
| Predicate | hasPort |
P35
|
FINISHED |
| Object | Luleå |
E160272
|
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: Luleå | Statement: [Gulf of Bothnia, hasPort, Luleå]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luleå Context triple: [Gulf of Bothnia, hasPort, Luleå]
-
A.
Luleå
chosen
Luleå is a coastal city in northern Sweden known for its major port, technology and university hub, and proximity to the Arctic Circle.
-
B.
Trollhättan
Trollhättan is a city in western Sweden known for its historic role in the automotive industry and as the longtime home of Saab Automobile’s main production facilities.
-
C.
Östersund
Östersund is a city in central Sweden known for its strong winter sports tradition and repeated bids to host the Winter Olympics.
-
D.
Gävle
Gävle is a coastal city in eastern Sweden known as an important regional port, industrial center, and the home of the famous Gävle Christmas Goat.
-
E.
Kiruna
Kiruna is a mining town in northern Sweden known for its large iron ore mine and its location above the Arctic Circle.
- 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_69a885e8caf88190a5fbb6159ce87786 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a907d7cbf48190be40590a7f9fa1de |
completed | March 5, 2026, 4:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae26de62d48190849540112614ccce |
completed | March 9, 2026, 1:48 a.m. |
Created at: March 4, 2026, 7:26 p.m.