Triple
T1725221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Misery |
E37480
|
entity |
| Predicate | notableCharacterTraitOfPaulSheldon |
P21469
|
FINISHED |
| Object | bestselling romance author |
—
|
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: bestselling romance author | Statement: [Misery, notableCharacterTraitOfPaulSheldon, bestselling romance author]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableCharacterTraitOfPaulSheldon Context triple: [Misery, notableCharacterTraitOfPaulSheldon, bestselling romance author]
-
A.
protagonistCharacteristic
chosen
Indicates that a characteristic, trait, or defining quality is attributed to the protagonist in a narrative or scenario.
-
B.
hasEnigmaticCharacter
Indicates that something possesses a mysterious, puzzling, or difficult-to-interpret quality or nature.
-
C.
hasSupportingCharacterTrait
Indicates that a supporting character possesses a particular trait, quality, or characteristic.
-
D.
coachingTrait
Indicates that one entity possesses a characteristic, style, or quality specifically related to coaching.
-
E.
personalityType
Indicates the specific psychological or behavioral profile that characterizes an entity’s typical patterns of thinking, feeling, and acting.
- 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_69a8861acab88190bb43cde203429399 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aadb7bda1081908f2c41c520c9c55c |
completed | March 6, 2026, 1:49 p.m. |
| PD | Predicate disambiguation | batch_69aa61c0a0288190bce9d60062a84b69 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:30 p.m.