Triple
T20324780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marta Vieira da Silva |
E492303
|
entity |
| Predicate | club |
P8194
|
FINISHED |
| Object | Umeå IK |
—
|
NE NERFINISHED |
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: Umeå IK | Statement: [Marta Vieira da Silva, club, Umeå IK]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Umeå IK Context triple: [Marta Vieira da Silva, club, Umeå IK]
-
A.
Umeå IK
chosen
Umeå IK is a Swedish sports club best known for its highly successful women's football team, which has won multiple national and European titles.
-
B.
IFK Umeå
IFK Umeå is a Swedish multi-sport club based in the city of Umeå, known for organizing and competing in various athletic disciplines.
-
C.
Vittsjö GIK
Vittsjö GIK is a Swedish football club best known for its women’s team competing in the top tiers of Swedish women’s football.
-
D.
IFK Östersund
IFK Östersund is a Swedish football club from Östersund, known locally for its traditional status and rivalry with Östersunds FK.
-
E.
Timrå IK
Timrå IK is a professional ice hockey club from Timrå, Sweden, best known for competing in the country’s top leagues and developing numerous elite players.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4a0134081909113563e1c3ba68a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6778e59508190bfd7a3ce44d56a93 |
completed | April 20, 2026, 6:59 p.m. |
Created at: April 16, 2026, 11:21 a.m.