Triple
T20096490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Orange Fairy Book |
E496413
|
entity |
| Predicate | hasApproximateNumberOfStories |
P25499
|
FINISHED |
| Object | 33 |
—
|
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: 33 | Statement: [The Orange Fairy Book, hasApproximateNumberOfStories, 33]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateNumberOfStories Context triple: [The Orange Fairy Book, hasApproximateNumberOfStories, 33]
-
A.
numberOfStories
Indicates the total count of levels or floors that a structure or building has.
-
B.
numberOfEmbeddedStories
Indicates the count of stories that are embedded within a given item or context.
-
C.
sectionCountApproximate
Indicates that the number of sections associated with an entity is known only approximately rather than as an exact count.
-
D.
numberOfMainStories
Indicates the total count of primary or main narrative segments associated with an entity.
-
E.
talesCount
chosen
Indicates the number of tales associated with or attributed to a given entity.
- 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_69da626eee3881909f3454986d4a6511 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6666cc02481908780a415b19c05a2 |
completed | April 20, 2026, 5:46 p.m. |
| PD | Predicate disambiguation | batch_69e54cf788188190a46cc49c9ce7617f |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 11:25 p.m.