Triple
T14957636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Princess and the Pea |
E372971
|
entity |
| Predicate | numberOfMattressesInTest |
P116841
|
FINISHED |
| Object | 20 |
—
|
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: 20 | Statement: [The Princess and the Pea, numberOfMattressesInTest, 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMattressesInTest Context triple: [The Princess and the Pea, numberOfMattressesInTest, 20]
-
A.
bedCount
Indicates the number of beds associated with an entity, such as a room, facility, or accommodation.
-
B.
hasBedMaterial
Indicates that one entity has, contains, or is characterized by a particular bed material (e.g., the substance forming the base or bedding of that entity).
-
C.
numberOfBedrooms
Indicates the quantity of bedrooms associated with a given property or dwelling.
-
D.
hasBedType
Indicates that an entity (such as a room or accommodation) is associated with a specific type or configuration of bed.
-
E.
bedLength
Indicates the measurement of how long a bed is from one end to the other.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6cc73848190ac181782b20dc838 |
completed | April 15, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69de9a5d995881909e33658f5aea5582 |
completed | April 14, 2026, 7:49 p.m. |
| PDg | Predicate description generation | batch_69deb1a4d8dc8190a4c0841c20f2875f |
completed | April 14, 2026, 9:29 p.m. |
Created at: April 10, 2026, 2:40 a.m.