Triple
T14622327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shake It Up |
E343254
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Tinka Hessenheffer
Tinka Hessenheffer is an eccentric, fashion-obsessed European exchange student and aspiring dancer on the Disney Channel series "Shake It Up."
|
E1176095
|
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: Tinka Hessenheffer | Statement: [Shake It Up, mainCharacter, Tinka Hessenheffer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tinka Hessenheffer Context triple: [Shake It Up, mainCharacter, Tinka Hessenheffer]
-
A.
Tanya Biank
Tanya Biank is an American journalist and author known for her in-depth reporting and books on the lives and challenges of military families.
-
B.
Kate Nauta
Kate Nauta is an American fashion model, actress, and singer best known for her role as the villainous Lola in the action film "Transporter 2."
-
C.
Kirsten Lees
Kirsten Lees is a prominent architect and partner at Grimshaw Architects, known for her leadership on major cultural and public projects.
-
D.
Lisa Wilhoit
Lisa Wilhoit is an American actress best known for her role on the cult teen drama series "My So-Called Life."
-
E.
Kirsten Nelson
Kirsten Nelson is an American actress best known for her role as police chief Karen Vick on the television series "Psych."
- 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: Tinka Hessenheffer Triple: [Shake It Up, mainCharacter, Tinka Hessenheffer]
Generated description
Tinka Hessenheffer is an eccentric, fashion-obsessed European exchange student and aspiring dancer on the Disney Channel series "Shake It Up."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tinka Hessenheffer Target entity description: Tinka Hessenheffer is an eccentric, fashion-obsessed European exchange student and aspiring dancer on the Disney Channel series "Shake It Up."
-
A.
Tanya Biank
Tanya Biank is an American journalist and author known for her in-depth reporting and books on the lives and challenges of military families.
-
B.
Kate Nauta
Kate Nauta is an American fashion model, actress, and singer best known for her role as the villainous Lola in the action film "Transporter 2."
-
C.
Kirsten Lees
Kirsten Lees is a prominent architect and partner at Grimshaw Architects, known for her leadership on major cultural and public projects.
-
D.
Lisa Wilhoit
Lisa Wilhoit is an American actress best known for her role on the cult teen drama series "My So-Called Life."
-
E.
Kirsten Nelson
Kirsten Nelson is an American actress best known for her role as police chief Karen Vick on the television series "Psych."
- 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_69d822dffc3c8190aa173b90761bffda |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb466a61c81908a110d40fb959b6f |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff908410548190ada5d4f71d52919b |
completed | May 9, 2026, 7:52 p.m. |
| NEDg | Description generation | batch_69ff9118272c8190a7b33fb312f37d39 |
completed | May 9, 2026, 7:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff9176f8208190ad88791592b35b72 |
completed | May 9, 2026, 7:56 p.m. |
Created at: April 10, 2026, 1:25 a.m.