Triple
T10445614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cloetta |
E246278
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object |
Ahlgrens bilar
Ahlgrens bilar is a popular Swedish candy brand best known for its small, car-shaped marshmallow sweets.
|
E864256
|
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: Ahlgrens bilar | Statement: [Cloetta, brand, Ahlgrens bilar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ahlgrens bilar Context triple: [Cloetta, brand, Ahlgrens bilar]
-
A.
Ola Auto
Ola Auto is a ride-hailing service segment of Ola Cabs that connects passengers with auto-rickshaw drivers via the Ola mobile app.
-
B.
Bilen
Bilen is a Cushitic language spoken primarily by the Bilen people in central Eritrea.
-
C.
Bilär
Bilär was a major medieval city that served as a key political and commercial center of Volga Bulgaria.
-
D.
Kockums Mekaniska Verkstad
Kockums Mekaniska Verkstad was a historic Swedish industrial and shipbuilding company based in Malmö, known for constructing naval vessels and submarines.
-
E.
Neoplan
Neoplan is a German bus and coach manufacturer renowned for its innovative, high-end touring and city buses.
- 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: Ahlgrens bilar Triple: [Cloetta, brand, Ahlgrens bilar]
Generated description
Ahlgrens bilar is a popular Swedish candy brand best known for its small, car-shaped marshmallow sweets.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ahlgrens bilar Target entity description: Ahlgrens bilar is a popular Swedish candy brand best known for its small, car-shaped marshmallow sweets.
-
A.
Ola Auto
Ola Auto is a ride-hailing service segment of Ola Cabs that connects passengers with auto-rickshaw drivers via the Ola mobile app.
-
B.
Bilen
Bilen is a Cushitic language spoken primarily by the Bilen people in central Eritrea.
-
C.
Bilär
Bilär was a major medieval city that served as a key political and commercial center of Volga Bulgaria.
-
D.
Kockums Mekaniska Verkstad
Kockums Mekaniska Verkstad was a historic Swedish industrial and shipbuilding company based in Malmö, known for constructing naval vessels and submarines.
-
E.
Neoplan
Neoplan is a German bus and coach manufacturer renowned for its innovative, high-end touring and city buses.
- 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_69d381c04fe08190957c26c526a3b05a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4fdbf81508190a160edea85105d3a |
completed | April 7, 2026, 12:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d87eeae8788190b63534fa4f942ead |
completed | April 10, 2026, 4:39 a.m. |
| NEDg | Description generation | batch_69d886c3fdcc8190a67a7f7788b8a2e8 |
completed | April 10, 2026, 5:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d88dc15ab481909011c5de93bbab14 |
completed | April 10, 2026, 5:42 a.m. |
Created at: April 6, 2026, 12:16 p.m.