Triple
T7554021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | E65 |
E178611
|
entity |
| Predicate | passesThroughCity |
P416
|
FINISHED |
| Object | Malmö |
E298741
|
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: Malmö | Statement: [E65, passesThroughCity, Malmö]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malmö Context triple: [E65, passesThroughCity, Malmö]
-
A.
Malmö
chosen
Malmö is a major coastal city in southern Sweden known for its historic center, modern architecture like the Turning Torso, and its role as a cultural and economic hub connected to Copenhagen via the Öresund Bridge.
-
B.
Sundsvall
Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
-
C.
Gothenburg
Gothenburg is a small city in western Nebraska known for its historic Pony Express station and classic Midwestern agricultural community.
-
D.
Gothenburg
Gothenburg is Sweden’s second-largest city, a major port on the country’s west coast known for its maritime heritage, universities, and vibrant cultural scene.
-
E.
Örebro
Örebro is a historic city in central Sweden known for its medieval castle, university, and role as a regional economic and cultural hub.
- 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_69c69f2da22c8190a50942ac20af70e8 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8b990148190b26a3a262cf538b3 |
completed | March 27, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c9a53a44e48190bddf0f4faec136e7 |
completed | March 29, 2026, 10:18 p.m. |
Created at: March 27, 2026, 3:49 p.m.