Triple
T15196630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malmö Airport |
E363152
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Sturup
Sturup is a locality in southern Sweden best known for being the site of Malmö Airport.
|
E1143317
|
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: Sturup | Statement: [Malmö Airport, locatedNear, Sturup]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sturup Context triple: [Malmö Airport, locatedNear, Sturup]
-
A.
Havelberg
Havelberg is a small historic town in Saxony-Anhalt, Germany, known for its medieval cathedral and location at the confluence of the Havel and Elbe rivers.
-
B.
Borghorst
Borghorst is a district of the German town Steinfurt in North Rhine-Westphalia, known historically for its textile industry and regional cultural heritage.
-
C.
Melchow
Melchow is a small municipality in the Barnim district of the federal state of Brandenburg in northeastern Germany.
-
D.
Torgelow
Torgelow is a small town in northeastern Germany’s Mecklenburg-Vorpommern region, known for its historical ironworks and surrounding forests and lakes.
-
E.
Hardegsen
Hardegsen is a small town in Lower Saxony, Germany, known for its medieval castle and historic town center.
- 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: Sturup Triple: [Malmö Airport, locatedNear, Sturup]
Generated description
Sturup is a locality in southern Sweden best known for being the site of Malmö Airport.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sturup Target entity description: Sturup is a locality in southern Sweden best known for being the site of Malmö Airport.
-
A.
Havelberg
Havelberg is a small historic town in Saxony-Anhalt, Germany, known for its medieval cathedral and location at the confluence of the Havel and Elbe rivers.
-
B.
Borghorst
Borghorst is a district of the German town Steinfurt in North Rhine-Westphalia, known historically for its textile industry and regional cultural heritage.
-
C.
Melchow
Melchow is a small municipality in the Barnim district of the federal state of Brandenburg in northeastern Germany.
-
D.
Torgelow
Torgelow is a small town in northeastern Germany’s Mecklenburg-Vorpommern region, known for its historical ironworks and surrounding forests and lakes.
-
E.
Hardegsen
Hardegsen is a small town in Lower Saxony, Germany, known for its medieval castle and historic town center.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0067fcc788190abdc083d4eadeb36 |
completed | April 15, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed3324fdc8190b31d4d2fcaffc57a |
completed | May 9, 2026, 6:24 a.m. |
| NEDg | Description generation | batch_69fed44b2e3c8190aad111e2bc2b56a2 |
completed | May 9, 2026, 6:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fed547192c8190b89755fff48ca620 |
completed | May 9, 2026, 6:33 a.m. |
Created at: April 10, 2026, 3:10 a.m.