Triple
T17219199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | If on a winter's night a traveler |
E417929
|
entity |
| Predicate | numberOfEmbeddedStories |
P126447
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [If on a winter's night a traveler, numberOfEmbeddedStories, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfEmbeddedStories Context triple: [If on a winter's night a traveler, numberOfEmbeddedStories, 10]
-
A.
numberOfStories
Indicates the total count of levels or floors that a structure or building has.
-
B.
numberOfMainStories
Indicates the total count of primary or main narrative segments associated with an entity.
-
C.
hasSiblingInStory
Indicates that one character in a narrative has at least one sibling who also appears within the same story.
-
D.
storyNumber
Indicates the numerical identifier assigned to a specific story within a collection, sequence, or dataset.
-
E.
numberOfNarrators
Indicates the quantity of distinct narrators associated with a given work or narrative.
- F. None of above. chosen
Provenance (4 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_69d886d779488190b131369541c04e7d |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42ddc3cb88190a67e35164d710d9d |
completed | April 19, 2026, 1:20 a.m. |
| PD | Predicate disambiguation | batch_69e3831e354881908c5505ffd15c84e9 |
completed | April 18, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69e3873f62108190966c4e741ebd548d |
completed | April 18, 2026, 1:29 p.m. |
Created at: April 10, 2026, 5:38 a.m.