Triple
T34542557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matsue Castle |
E886839
|
entity |
| Predicate | hasNumberOfMainKeepStories |
P125599
|
FINISHED |
| Object | 5 external stories |
—
|
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: 5 external stories | Statement: [Matsue Castle, hasNumberOfMainKeepStories, 5 external stories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfMainKeepStories Context triple: [Matsue Castle, hasNumberOfMainKeepStories, 5 external stories]
-
A.
numberOfMainStories
chosen
Indicates the total count of primary or main narrative segments associated with an entity.
-
B.
numberOfStories
Indicates the total count of levels or floors that a structure or building has.
-
C.
numberOfEmbeddedStories
Indicates the count of stories that are embedded within a given item or context.
-
D.
numberOfMainTexts
Indicates the quantity of primary or main textual components associated with an entity.
-
E.
intendedNumberOfStories
Indicates the planned or designed count of stories (floors) that a structure is intended to have.
- 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_69f349ce5eb881909e431c670944aa68 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69feafa1ba0081909013800b85a9f613 |
completed | May 9, 2026, 3:53 a.m. |
| PD | Predicate disambiguation | batch_69feae58d62c81909d031f3df8992883 |
completed | May 9, 2026, 3:47 a.m. |
Created at: May 1, 2026, 2:02 a.m.