Triple
T10647718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Street (United Kingdom) |
E250879
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Mark Benton |
E354410
|
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: Mark Benton | Statement: [The Street (United Kingdom), hasCastMember, Mark Benton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Benton Context triple: [The Street (United Kingdom), hasCastMember, Mark Benton]
-
A.
Mark Benton
chosen
Mark Benton is an English character actor known for his extensive work in British television drama and comedy, as well as stage and film roles.
-
B.
Mark Bish
Mark Bish is the son of American actress Rue McClanahan, best known for her role as Blanche Devereaux on the television series "The Golden Girls."
-
C.
Mark Suter
Mark Suter is a percussionist known for his work in contemporary and world music, including performances with the Silk Road Ensemble.
-
D.
Grant Bardsley
Grant Bardsley is a British voice actor best known for voicing the protagonist Taran in Disney’s animated film "The Black Cauldron."
-
E.
Chris Gill
Chris Gill is a British film editor best known for his work on the acclaimed horror film "28 Days Later" and other notable UK productions.
- 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_69d6aa5a4c4881908f39be6efe5981e5 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6dfe29b8081908eb13637e0475ba1 |
completed | April 8, 2026, 11:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d97a580d388190aea5edadd4afc0d1 |
completed | April 10, 2026, 10:31 p.m. |
Created at: April 8, 2026, 9:05 p.m.