Triple
T16865873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Hans Zarkov |
E410034
|
entity |
| Predicate | nationalityInManyAdaptations |
P47716
|
FINISHED |
| Object | Earth human |
—
|
LITERAL 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: Earth human | Statement: [Dr. Hans Zarkov, nationalityInManyAdaptations, Earth human]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nationalityInManyAdaptations Context triple: [Dr. Hans Zarkov, nationalityInManyAdaptations, Earth human]
-
A.
nationalityInStory
Indicates that a character or entity in a narrative is associated with a particular nationality within the context of that story.
-
B.
nationalityInHumanWorld
chosen
Indicates that one entity has the specified national affiliation or citizenship within the context of the human world.
-
C.
televisionAdaptationCountry
Indicates the country in which a television adaptation of a work was produced or primarily created.
-
D.
hasNotableAdaptationBy
Indicates that an original work has a significant adaptation created by the specified adapting entity (such as a person, group, or organization).
-
E.
adaptedInLanguage
Indicates that a work or content has been modified or translated so it can be presented or understood in a specified language.
- F. None of above.
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_69e3b5088f208190abfe937633ebe3fe |
completed | April 18, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69e32b8cbb048190878a259cc5be960e |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:24 a.m.