Triple
T14107450
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How the Leopard Got His Spots |
E339542
|
entity |
| Predicate | collectionOrderInJustSoStories |
P50204
|
FINISHED |
| Object | one of the stories in the collection |
—
|
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: one of the stories in the collection | Statement: [How the Leopard Got His Spots, collectionOrderInJustSoStories, one of the stories in the collection]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: collectionOrderInJustSoStories Context triple: [How the Leopard Got His Spots, collectionOrderInJustSoStories, one of the stories in the collection]
-
A.
numberOfStories
Indicates the total count of levels or floors that a structure or building has.
-
B.
storyNumber
chosen
Indicates the numerical identifier assigned to a specific story within a collection, sequence, or dataset.
-
C.
storyBy
Indicates that one entity is the creator or author of the story associated with another entity.
-
D.
readingOrganizer
Indicates that an entity is responsible for planning, managing, or coordinating reading-related activities or materials for others.
-
E.
originStoryIncludes
Indicates that an entity’s origin story contains, involves, or features the referenced element as a component or part of that backstory.
- 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.