Triple
T24206456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Criss |
E600417
|
entity |
| Predicate | characterPersona |
P155206
|
FINISHED |
| Object | Catman |
—
|
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: Catman | Statement: [Peter Criss, characterPersona, Catman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterPersona Context triple: [Peter Criss, characterPersona, Catman]
-
A.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
B.
characterSetting
Indicates that a character is associated with, appears in, or is situated within a particular setting or environment.
-
C.
characterDescription
Indicates that one entity provides a textual description or portrayal of the characteristics, traits, or attributes of another entity.
-
D.
character3
Indicates a tertiary or additional character role associated with an entity, typically the third distinct character linked within a given context or work.
-
E.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
- F. None of above. chosen
Provenance (4 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_69e2953344c48190875730c7d52112a0 |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f27ca63d188190add6c41929bb5cb5 |
completed | April 29, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f1c43e55688190b55fc20274ed471c |
completed | April 29, 2026, 8:41 a.m. |
| PDg | Predicate description generation | batch_69f1c9834064819082024233d9c6f98f |
completed | April 29, 2026, 9:04 a.m. |
Created at: April 17, 2026, 11:42 p.m.