Triple
T24976813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robin Hood |
E625043
|
entity |
| Predicate | frameGeometry |
P158698
|
FINISHED |
| Object | upright riding position |
—
|
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: upright riding position | Statement: [Robin Hood, frameGeometry, upright riding position]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frameGeometry Context triple: [Robin Hood, frameGeometry, upright riding position]
-
A.
frameShape
Indicates that one entity has the specified geometric or structural shape of a frame in relation to another entity.
-
B.
frameSize
Indicates the size or dimensions of a frame associated with an entity.
-
C.
screenGeometry
Indicates the spatial layout or dimensions of a screen or display area relative to a defined coordinate system.
-
D.
frame
Indicates placing or presenting something within a particular context, structure, or perspective that shapes how it is interpreted.
-
E.
frameWidth
Indicates the measurement of how wide a frame is, typically specifying its horizontal extent or thickness.
- 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_69e2ff254570819093d197b1900305ac |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f47b865df48190bf4b6d3e9f9305e6 |
completed | May 1, 2026, 10:08 a.m. |
| PD | Predicate disambiguation | batch_69f4682c8a3c8190adbfaac99474eaaf |
completed | May 1, 2026, 8:45 a.m. |
| PDg | Predicate description generation | batch_69f47b7f657c81908174590c811a3cbf |
completed | May 1, 2026, 10:07 a.m. |
Created at: April 18, 2026, 6:02 a.m.