Triple
T1287678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uncle Fred stories |
E27471
|
entity |
| Predicate | characterTraitOfProtagonist |
P21469
|
FINISHED |
| Object | mischievous |
—
|
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: mischievous | Statement: [Uncle Fred stories, characterTraitOfProtagonist, mischievous]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterTraitOfProtagonist Context triple: [Uncle Fred stories, characterTraitOfProtagonist, mischievous]
-
A.
protagonistCharacteristic
chosen
Indicates that a characteristic, trait, or defining quality is attributed to the protagonist in a narrative or scenario.
-
B.
protagonistType
Indicates the role or category that the main character (protagonist) of a story or scenario belongs to.
-
C.
featuresCharacterRole
Indicates that a work includes a character appearing in a specific narrative or functional role.
-
D.
characterAlignment
Indicates the moral or ethical stance a character holds, typically along axes such as good–evil and lawful–chaotic.
-
E.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
- 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_69a496d4ec448190ad653b2590c46711 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c0d1a5508190b4461df77f560df4 |
completed | March 1, 2026, 10:42 p.m. |
| PD | Predicate disambiguation | batch_69a4bee41ca08190b0ad6f7ea40c0b62 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:51 p.m.