Triple
T12901578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seven of Nine |
E308623
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object |
Annika Hansen
Annika Hansen is the human birth name of Seven of Nine, a former Borg drone and prominent character in the Star Trek franchise.
|
E1008177
|
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: Annika Hansen | Statement: [Seven of Nine, fullName, Annika Hansen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Annika Hansen Context triple: [Seven of Nine, fullName, Annika Hansen]
-
A.
Annika
Annika is a television crime drama series featuring Kate Dickie in a prominent role.
-
B.
Annika Persson
Annika Persson is a notable individual who shares the Persson surname, which is common in Sweden and associated with several prominent figures in politics, business, and culture.
-
C.
Annika Lammers
Annika Lammers is a person notable enough to be recognized as a bearer of the surname Lammers.
-
D.
Emma Grede
Emma Grede is a British entrepreneur and fashion executive best known as the co-founder and CEO of Good American and a founding partner of Kim Kardashian’s shapewear brand SKIMS.
-
E.
Camille Groth
Camille Groth is an individual notable enough to be recognized as a bearer of the surname Groth, though specific widely known public details about them are not readily available.
- 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: Annika Hansen Triple: [Seven of Nine, fullName, Annika Hansen]
Generated description
Annika Hansen is the human birth name of Seven of Nine, a former Borg drone and prominent character in the Star Trek franchise.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Annika Hansen Target entity description: Annika Hansen is the human birth name of Seven of Nine, a former Borg drone and prominent character in the Star Trek franchise.
-
A.
Annika
Annika is a television crime drama series featuring Kate Dickie in a prominent role.
-
B.
Annika Persson
Annika Persson is a notable individual who shares the Persson surname, which is common in Sweden and associated with several prominent figures in politics, business, and culture.
-
C.
Annika Lammers
Annika Lammers is a person notable enough to be recognized as a bearer of the surname Lammers.
-
D.
Emma Grede
Emma Grede is a British entrepreneur and fashion executive best known as the co-founder and CEO of Good American and a founding partner of Kim Kardashian’s shapewear brand SKIMS.
-
E.
Camille Groth
Camille Groth is an individual notable enough to be recognized as a bearer of the surname Groth, though specific widely known public details about them are not readily available.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97180ee708190b60a3e58c42f764f |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a56189b081909ed838addcb6d265 |
completed | May 3, 2026, 1:31 a.m. |
| NEDg | Description generation | batch_69f6a6179cdc8190976daa1384032445 |
completed | May 3, 2026, 1:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6a6cbec348190a96a0194b2d6be4b |
completed | May 3, 2026, 1:37 a.m. |
Created at: April 9, 2026, 5:40 p.m.