Triple
T20324785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marta Vieira da Silva |
E492303
|
entity |
| Predicate | club |
P8194
|
FINISHED |
| Object | Rosengård |
—
|
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: Rosengård | Statement: [Marta Vieira da Silva, club, Rosengård]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosengård Context triple: [Marta Vieira da Silva, club, Rosengård]
-
A.
Rosengård
chosen
Rosengård is a prominent Swedish football club based in Malmö, known for its successful women's team and history of developing world-class players.
-
B.
Rosersberg
Rosersberg is a locality in Stockholm County, Sweden, known for its historic Rosersberg Palace and its location near Stockholm Arlanda Airport.
-
C.
Häggenås
Häggenås is a small locality in Jämtland County, northern Sweden, situated within Östersund Municipality.
-
D.
Grubbegata
Grubbegata is a street in central Oslo, Norway, known for running through the area that houses key government buildings and institutions.
-
E.
Djursholm
Djursholm is an affluent suburban district of Stockholm, Sweden, known for its villas, garden-city planning, and status as one of the country’s wealthiest residential areas.
- 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.