Triple
T14462423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rebecca Front |
E358616
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Front
Front is a British actress, writer, and comedian best known for her roles in television series such as "The Thick of It" and "Nighty Night."
|
E1101115
|
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: Front | Statement: [Rebecca Front, familyName, Front]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Front Context triple: [Rebecca Front, familyName, Front]
-
A.
Full Frontal
Full Frontal is an Australian sketch comedy television series that helped launch the career of actor Eric Bana.
-
B.
Frontale
Frontale is a professional Japanese football club based in Kawasaki, competing in the J1 League.
-
C.
Front Row
Front Row is a full-screen media center application for Mac computers that provided an easy-to-use interface for browsing and playing music, videos, photos, and DVDs with a remote control.
-
D.
Front Row
Front Row is a Philippine documentary television program produced by GMA News and Public Affairs that features in-depth, human-interest stories and social issues.
-
E.
Forward
Forward is the state motto of Wisconsin, expressing its emphasis on progress and continual improvement.
- 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: Front Triple: [Rebecca Front, familyName, Front]
Generated description
Front is a British actress, writer, and comedian best known for her roles in television series such as "The Thick of It" and "Nighty Night."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Front Target entity description: Front is a British actress, writer, and comedian best known for her roles in television series such as "The Thick of It" and "Nighty Night."
-
A.
Full Frontal
Full Frontal is an Australian sketch comedy television series that helped launch the career of actor Eric Bana.
-
B.
Frontale
Frontale is a professional Japanese football club based in Kawasaki, competing in the J1 League.
-
C.
Front Row
Front Row is a full-screen media center application for Mac computers that provided an easy-to-use interface for browsing and playing music, videos, photos, and DVDs with a remote control.
-
D.
Front Row
Front Row is a Philippine documentary television program produced by GMA News and Public Affairs that features in-depth, human-interest stories and social issues.
-
E.
Forward
Forward is the state motto of Wisconsin, expressing its emphasis on progress and continual improvement.
- 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_69d82794dfa081909b9134ad2e32244b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91ad67bc81908ecdaa7262f6dc55 |
completed | April 14, 2026, 7:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd64973dc08190ab893c95ea3f066c |
completed | May 8, 2026, 4:20 a.m. |
| NEDg | Description generation | batch_69fd67b1ed2081908d3de6514078be49 |
completed | May 8, 2026, 4:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd682f28948190adc037c18c7deb93 |
completed | May 8, 2026, 4:35 a.m. |
Created at: April 10, 2026, 1:19 a.m.