Triple
T11175844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marta |
E264408
|
entity |
| Predicate | club |
P8194
|
FINISHED |
| Object |
Rosengård
Rosengård is a prominent Swedish football club based in Malmö, known for its successful women's team and history of developing world-class players.
|
E910502
|
NE FINISHED |
How this triple was built (4 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, club, Rosengård]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosengård Context triple: [Marta, club, Rosengård]
-
A.
Rosersberg
Rosersberg is a locality in Stockholm County, Sweden, known for its historic Rosersberg Palace and its location near Stockholm Arlanda Airport.
-
B.
Häggenås
Häggenås is a small locality in Jämtland County, northern Sweden, situated within Östersund Municipality.
-
C.
Grubbegata
Grubbegata is a street in central Oslo, Norway, known for running through the area that houses key government buildings and institutions.
-
D.
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.
-
E.
Hjulsta
Hjulsta is a suburb in northwestern Stockholm, Sweden, known for being the terminus of one of the Stockholm metro lines.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Rosengård Triple: [Marta, club, Rosengård]
Generated description
Rosengård is a prominent Swedish football club based in Malmö, known for its successful women's team and history of developing world-class players.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rosengård Target entity description: Rosengård is a prominent Swedish football club based in Malmö, known for its successful women's team and history of developing world-class players.
-
A.
Rosersberg
Rosersberg is a locality in Stockholm County, Sweden, known for its historic Rosersberg Palace and its location near Stockholm Arlanda Airport.
-
B.
Häggenås
Häggenås is a small locality in Jämtland County, northern Sweden, situated within Östersund Municipality.
-
C.
Grubbegata
Grubbegata is a street in central Oslo, Norway, known for running through the area that houses key government buildings and institutions.
-
D.
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.
-
E.
Hjulsta
Hjulsta is a suburb in northwestern Stockholm, Sweden, known for being the terminus of one of the Stockholm metro lines.
- F. None of above. chosen
Provenance (5 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_69d6aa9dafac8190bd90d2c74f661aa7 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8987e1081909b28a0bdb866beae |
completed | April 9, 2026, 5:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4838f19388190af6fde7d4275ce2a |
completed | April 19, 2026, 7:26 a.m. |
| NEDg | Description generation | batch_69e48788be688190a109ccb8281d3dc9 |
completed | April 19, 2026, 7:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e4890c12388190838d350207492c9e |
completed | April 19, 2026, 7:49 a.m. |
Created at: April 8, 2026, 9:29 p.m.