Triple
T15305877
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Career Girls |
E365895
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Ralph Brown |
E437346
|
NE FINISHED |
How this triple was built (2 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: Ralph Brown | Statement: [Career Girls, hasCastMember, Ralph Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ralph Brown Context triple: [Career Girls, hasCastMember, Ralph Brown]
-
A.
Ralph Brown
chosen
Ralph Brown is a British actor best known for his memorable character roles in films such as "Withnail & I," "Alien 3," and "Wayne's World 2."
-
B.
Ralph Brownrigg
Ralph Brownrigg was a 17th-century English clergyman and academic who served as Bishop of Exeter in the Church of England.
-
C.
Don Hubbard
Don Hubbard is an individual notable enough to be recognized as a significant bearer of the surname Hubbard, though specific widely known public details about him are limited.
-
D.
Vinton Harper
Vinton Harper is a bumbling, good-natured son of Thelma "Mama" Harper and a central source of comic relief in the sitcom "Mama’s Family."
-
E.
Tom B. Brown
Tom B. Brown is a machine learning researcher known for leading work on large-scale language models, including the influential GPT-3 paper "Language Models are Few-Shot Learners."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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. |
Created at: April 10, 2026, 3:16 a.m.