Triple
T15305869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Career Girls |
E365895
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Kate Byers
Kate Byers is an actress known for her role in the British television series "Career Girls."
|
E1150622
|
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: Kate Byers | Statement: [Career Girls, hasCastMember, Kate Byers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Byers Context triple: [Career Girls, hasCastMember, Kate Byers]
-
A.
Kate Veatch
Kate Veatch is a sharp, athletic lawyer and love interest who joins the Average Joes team in the sports comedy film "Dodgeball: A True Underdog Story."
-
B.
Megan Beyer
Megan Beyer is an American journalist and civic leader known for her work in cultural diplomacy, gender equality, and public policy initiatives.
-
C.
Jennifer Morris
Jennifer Morris is a conservation leader who serves as the chief executive officer of the global environmental nonprofit Conservation International.
-
D.
Allison Feaster
Allison Feaster is a former American professional basketball player best known for her standout WNBA career and later work as an NBA front office executive.
-
E.
Erin Burkett
Erin Burkett is an American music industry executive and co-founder of the influential punk rock record label Fat Wreck Chords.
- 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: Kate Byers Triple: [Career Girls, hasCastMember, Kate Byers]
Generated description
Kate Byers is an actress known for her role in the British television series "Career Girls."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kate Byers Target entity description: Kate Byers is an actress known for her role in the British television series "Career Girls."
-
A.
Kate Veatch
Kate Veatch is a sharp, athletic lawyer and love interest who joins the Average Joes team in the sports comedy film "Dodgeball: A True Underdog Story."
-
B.
Megan Beyer
Megan Beyer is an American journalist and civic leader known for her work in cultural diplomacy, gender equality, and public policy initiatives.
-
C.
Jennifer Morris
Jennifer Morris is a conservation leader who serves as the chief executive officer of the global environmental nonprofit Conservation International.
-
D.
Allison Feaster
Allison Feaster is a former American professional basketball player best known for her standout WNBA career and later work as an NBA front office executive.
-
E.
Erin Burkett
Erin Burkett is an American music industry executive and co-founder of the influential punk rock record label Fat Wreck Chords.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03ccef14c819099c5ebe962e7f867 |
completed | April 16, 2026, 1:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef89d961481909be8dcc2864982c9 |
completed | May 9, 2026, 9:04 a.m. |
| NEDg | Description generation | batch_69fefa339f988190b470e052c853e4f8 |
completed | May 9, 2026, 9:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fefac48df08190ad58e9d455546a57 |
completed | May 9, 2026, 9:13 a.m. |
Created at: April 10, 2026, 3:16 a.m.