Triple
T5334394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Derek |
E123789
|
entity |
| Predicate | photographed |
P12333
|
FINISHED |
| Object | Bo Derek |
E512447
|
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: Bo Derek | Statement: [John Derek, photographed, Bo Derek]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bo Derek Context triple: [John Derek, photographed, Bo Derek]
-
A.
Bo Derek
chosen
Bo Derek is an American actress and model best known for her breakout role in the 1979 film "10," which made her a major sex symbol of the late 20th century.
-
B.
Salman Khan
Salman Khan is an American educator and entrepreneur best known as the founder of the online learning platform Khan Academy.
-
C.
Sunil Dutt
Sunil Dutt was a prominent Indian film actor, producer, and politician known for his humanitarian work and long association with the Indian National Congress.
-
D.
Shah Rukh Khan
Shah Rukh Khan is a hugely influential Indian film actor and producer, often called the "King of Bollywood," known for his prolific career in Hindi cinema and global cultural impact.
-
E.
Akshaye Khanna
Akshaye Khanna is an Indian film actor known for his versatile performances in Hindi cinema across both commercial hits and critically acclaimed dramas.
- 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_69bd464b07f8819095aa76577c9829e4 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85ae52c08190968a5567b7e6b794 |
completed | March 20, 2026, 5:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf21bebea0819083e6deae67f3e834 |
completed | March 21, 2026, 10:54 p.m. |
Created at: March 20, 2026, 2 p.m.