Triple
T14107447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How the Leopard Got His Spots |
E339542
|
entity |
| Predicate | fictionalAnimalDepicted |
P42281
|
FINISHED |
| Object | leopard |
—
|
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: leopard | Statement: [How the Leopard Got His Spots, fictionalAnimalDepicted, leopard]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalAnimalDepicted Context triple: [How the Leopard Got His Spots, fictionalAnimalDepicted, leopard]
-
A.
fictionalSpecies
Indicates that the subject is a species that exists only in fiction or imaginary works, rather than in real life.
-
B.
animalProtagonist
Indicates that the main character or central figure in a narrative is an animal.
-
C.
speciesInspiredBy
Indicates that one species concept, design, or depiction is modeled after, influenced by, or creatively derived from another species.
-
D.
featuresAnimalsFrom
Indicates that one entity includes, presents, or showcases animals originating from or associated with another entity.
-
E.
animalTypeFeatured
chosen
Indicates that a particular type or category of animal is highlighted or prominently showcased in a given 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_69d81c69b5c8819094aa1abf18302908 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de600ada808190b92d67dc30f13d15 |
completed | April 14, 2026, 3:40 p.m. |
| PD | Predicate disambiguation | batch_69de05b2f7e481908a9a7d40153234c0 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:22 p.m.