Triple
T2512610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | East Lansing |
E52734
|
entity |
| Predicate | hasSisterCity |
P919
|
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: [East Lansing, hasSisterCity, Luleå]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luleå Context triple: [East Lansing, hasSisterCity, 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.
Umeå
Umeå is a university city in northern Sweden known for its cultural scene, research institutions, and role as a regional economic hub.
-
C.
Gällivare
Gällivare is a mining town in northern Sweden known for its significant iron ore deposits and role in the region’s mining industry.
-
D.
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.
-
E.
Östersund
Östersund is a city in central Sweden known for its strong winter sports tradition and repeated bids to host the Winter Olympics.
- 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_69ab4958e76481908a235377dd921c9e |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1efb5c48190a9b47b39a388412b |
completed | March 7, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b03104c21c81909fede29ce1e76d88 |
completed | March 10, 2026, 2:56 p.m. |
Created at: March 6, 2026, 9:46 p.m.