Triple
T21877978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Batey |
E540201
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Mark Batey
Mark Batey is a notable individual recognized for his contributions in his professional field, likely in academia or industry.
|
E1514323
|
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: Mark Batey | Statement: [Batey, hasNotableBearer, Mark Batey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Batey Context triple: [Batey, hasNotableBearer, Mark Batey]
-
A.
Keith Batey
Keith Batey was a British codebreaker and intelligence officer who worked at Bletchley Park during World War II, notably alongside his wife, fellow cryptanalyst Mavis Batey.
-
B.
Bryan Bedford
Bryan Bedford is an American airline executive best known for leading regional carriers such as Chautauqua Airlines and Republic Airways Holdings.
-
C.
Bryan Bedford
Bryan Bedford is the young boy central to the 1994 film "Miracle on 34th Street," whose belief in Santa Claus becomes a key focus of the story.
-
D.
Bryan Bedford
Bryan Bedford is a central character in the film "Miracle on 34th Street," serving as the caring and supportive stepfather figure to Susan Walker.
-
E.
Brent Judd
Brent Judd is a film and television producer best known for his work on the comedy series "Trainwreck."
- 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: Mark Batey Triple: [Batey, hasNotableBearer, Mark Batey]
Generated description
Mark Batey is a notable individual recognized for his contributions in his professional field, likely in academia or industry.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mark Batey Target entity description: Mark Batey is a notable individual recognized for his contributions in his professional field, likely in academia or industry.
-
A.
Keith Batey
Keith Batey was a British codebreaker and intelligence officer who worked at Bletchley Park during World War II, notably alongside his wife, fellow cryptanalyst Mavis Batey.
-
B.
Bryan Bedford
Bryan Bedford is an American airline executive best known for leading regional carriers such as Chautauqua Airlines and Republic Airways Holdings.
-
C.
Bryan Bedford
Bryan Bedford is the young boy central to the 1994 film "Miracle on 34th Street," whose belief in Santa Claus becomes a key focus of the story.
-
D.
Bryan Bedford
Bryan Bedford is a central character in the film "Miracle on 34th Street," serving as the caring and supportive stepfather figure to Susan Walker.
-
E.
Brent Judd
Brent Judd is a film and television producer best known for his work on the comedy series "Trainwreck."
- 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_69e0c479a98081908ce333853fdd4348 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0f33c012c819096d0f7b2ffdc7a2f |
completed | April 28, 2026, 5:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a7361a24081908b1334e2ad9923f9 |
completed | May 18, 2026, 2:03 a.m. |
| NEDg | Description generation | batch_6a0a780f0c008190bd56b7af0bebbc85 |
completed | May 18, 2026, 2:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a78c2a94c8190841463b0a52ba16e |
completed | May 18, 2026, 2:26 a.m. |
Created at: April 16, 2026, 7:03 p.m.