Triple
T16865659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chapter 2: Freezing Torture |
E410028
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Dr. Zarkov |
E410034
|
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: Dr. Zarkov | Statement: [Chapter 2: Freezing Torture, featuresCharacter, Dr. Zarkov]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dr. Zarkov Context triple: [Chapter 2: Freezing Torture, featuresCharacter, Dr. Zarkov]
-
A.
Dr. Hans Zarkov
chosen
Dr. Hans Zarkov is a brilliant but eccentric scientist and ally of Flash Gordon in the classic science fiction adventure series.
-
B.
Valerian Zorin
Valerian Zorin was a Soviet diplomat and politician best known for his defiant role as the USSR’s representative to the United Nations during the Cold War.
-
C.
Dr. Petrov
Dr. Petrov is a minor Soviet medical officer aboard the submarine Red October in Tom Clancy’s Cold War thriller "The Hunt for Red October."
-
D.
Max Zorin
Max Zorin is the main villain in the James Bond film "A View to a Kill," a ruthless industrialist plotting to destroy Silicon Valley for financial gain.
-
E.
Dr. Kosevich
Dr. Kosevich is a comedic obstetrician character in the 1995 romantic comedy film "Nine Months."
- 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_69d88395e6c88190b22730f335107c14 |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b506dd1c81909ab8006b6a1e2b7a |
completed | April 18, 2026, 4:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00bb294c0481908306e1f604cc7404 |
completed | May 10, 2026, 5:06 p.m. |
Created at: April 10, 2026, 5:24 a.m.