Triple
T37718401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USA Comics #1 |
E939515
|
entity |
| Predicate | coverFeatureCharacter |
P93957
|
FINISHED |
| Object | Whizzer |
—
|
NE NERFINISHED |
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: Whizzer | Statement: [USA Comics #1, coverFeatureCharacter, Whizzer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coverFeatureCharacter Context triple: [USA Comics #1, coverFeatureCharacter, Whizzer]
-
A.
coverRole
Indicates that one entity temporarily assumes or fills the role, duties, or position normally held by another entity.
-
B.
featuresCharacterWith
chosen
Indicates that one entity (such as a work or product) includes or presents a particular character as part of its content.
-
C.
featuresReturningCharacterFrom
Indicates that a work includes the reappearance of a character who previously appeared in the referenced source work.
-
D.
coversPerson
Indicates that one entity provides protection, insurance, or inclusion for a person under its scope or terms.
-
E.
characterInFocus
Indicates that a particular character is the primary subject or focal point within a given context, scene, or narrative segment.
- 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_69f76edc208c8190bc8b9683f75e1024 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fd64bc86848190a49f451a8fc5cf1e |
completed | May 8, 2026, 4:21 a.m. |
| PD | Predicate disambiguation | batch_69fd5ff4a648819090756d90fd195d9a |
completed | May 8, 2026, 4 a.m. |
Created at: May 3, 2026, 4:18 p.m.