Triple
T13566248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Jepsen |
E324041
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object |
Anna Jepsen
Anna Jepsen is a person whose specific public background or notable achievements are not clearly identifiable from the given information.
|
E324041
|
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: Anna Jepsen | Statement: [Anna Jepsen, name, Anna Jepsen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Jepsen Context triple: [Anna Jepsen, name, Anna Jepsen]
-
A.
Anna Jepsen
Anna Jepsen is a person notable enough to be recognized as a prominent bearer of the surname Jepsen.
-
B.
Mary Lou Jepsen
Mary Lou Jepsen is an American engineer, inventor, and entrepreneur known for her pioneering work in display technology and for co-founding the low-cost computing initiative One Laptop per Child.
-
C.
Carly Rae Jepsen
Carly Rae Jepsen is a Canadian pop singer and songwriter best known for her global hit single "Call Me Maybe" and her critically acclaimed album "Emotion."
-
D.
Juno Skinner
Juno Skinner is a seductive and ruthless art dealer who secretly collaborates with terrorists in the action film "True Lies."
-
E.
Lykke Li
Lykke Li is a Swedish indie pop singer-songwriter known for her atmospheric, melancholic sound and hits like "I Follow Rivers."
- 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: Anna Jepsen Triple: [Anna Jepsen, name, Anna Jepsen]
Generated description
Anna Jepsen is a person whose specific public background or notable achievements are not clearly identifiable from the given information.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anna Jepsen Target entity description: Anna Jepsen is a person whose specific public background or notable achievements are not clearly identifiable from the given information.
-
A.
Anna Jepsen
chosen
Anna Jepsen is a person notable enough to be recognized as a prominent bearer of the surname Jepsen.
-
B.
Mary Lou Jepsen
Mary Lou Jepsen is an American engineer, inventor, and entrepreneur known for her pioneering work in display technology and for co-founding the low-cost computing initiative One Laptop per Child.
-
C.
Carly Rae Jepsen
Carly Rae Jepsen is a Canadian pop singer and songwriter best known for her global hit single "Call Me Maybe" and her critically acclaimed album "Emotion."
-
D.
Juno Skinner
Juno Skinner is a seductive and ruthless art dealer who secretly collaborates with terrorists in the action film "True Lies."
-
E.
Lykke Li
Lykke Li is a Swedish indie pop singer-songwriter known for her atmospheric, melancholic sound and hits like "I Follow Rivers."
- F. None of above.
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_69d8076830b48190910a902bae5888e2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb00cecd48190a9a2caff3d424817 |
completed | April 12, 2026, 2:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75db031d88190983e3ccd054082bd |
completed | May 3, 2026, 2:37 p.m. |
| NEDg | Description generation | batch_69f75e4222d481909781824cefb69b49 |
completed | May 3, 2026, 2:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f75e9fc1508190800291a16840a9a8 |
completed | May 3, 2026, 2:41 p.m. |
Created at: April 9, 2026, 9:48 p.m.