Triple
T24069532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nine Swimming Pools and a Broken Glass |
E596185
|
entity |
| Predicate | numberOfBrokenGlassImages |
P62360
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Nine Swimming Pools and a Broken Glass, numberOfBrokenGlassImages, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfBrokenGlassImages Context triple: [Nine Swimming Pools and a Broken Glass, numberOfBrokenGlassImages, 1]
-
A.
numberOfGlassPanes
Indicates the quantity of individual glass panes associated with or contained in an object or structure.
-
B.
numberOfStainedGlassPanels
Indicates the count of stained glass panels associated with a given entity or object.
-
C.
numberOfStills
chosen
Indicates the quantity of still images associated with or contained in a given entity or context.
-
D.
glassType
Indicates the specific kind or category of glass associated with or used by an entity.
-
E.
numberOfFissures
Indicates the count of distinct fissures associated with a given entity or structure.
- 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_69e288c25c008190850cf447940ab181 |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1db17c99881909f97e858fb183d86 |
completed | April 29, 2026, 10:19 a.m. |
| PD | Predicate disambiguation | batch_69f1764b1d4c8190b12590c6339c31c1 |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 10:41 p.m.