Triple
T27945786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FDDI |
E700874
|
entity |
| Predicate | hasFrameSize |
P39914
|
FINISHED |
| Object | up to 4500 bytes |
—
|
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: up to 4500 bytes | Statement: [FDDI, hasFrameSize, up to 4500 bytes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFrameSize Context triple: [FDDI, hasFrameSize, up to 4500 bytes]
-
A.
inputFrameSize
Indicates the size or dimensions of the input frame used or processed in a given context.
-
B.
frameSize
chosen
Indicates the size or dimensions of a frame associated with an entity.
-
C.
hasFrameElement
Indicates that a frame (or structured conceptual scenario) includes or is associated with a specific frame element (a participant, role, or component within that frame).
-
D.
hasSize
Indicates that one entity possesses a particular physical magnitude or extent, such as length, volume, or overall dimensions.
-
E.
hasFrameType
Indicates that an entity possesses or is associated with a specific type or category of frame.
- 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_69ef6a5028108190a14696d9821dde49 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f67257b0448190a13011af81c81449 |
completed | May 2, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69f66ec3d3d48190ab2f2b71939e572e |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 27, 2026, 7:21 p.m.