Triple
T7374976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zapruder film |
E170100
|
entity |
| Predicate | numberOfFrames |
P62360
|
FINISHED |
| Object | 486 |
—
|
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: 486 | Statement: [Zapruder film, numberOfFrames, 486]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFrames Context triple: [Zapruder film, numberOfFrames, 486]
-
A.
frameDuration
Indicates the length of time that a single frame in a sequence (such as video or animation) is displayed before advancing to the next frame.
-
B.
numberOfStills
chosen
Indicates the quantity of still images associated with or contained in a given entity or context.
-
C.
fieldsPerFrame
Indicates the number of discrete fields that compose each video frame in an interlaced video signal.
-
D.
filmStripCount
Indicates the number of film strips associated with or contained in a given entity or context.
-
E.
framesAs
Indicates how one entity presents, characterizes, or interprets another entity or situation in a particular light or context.
- 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_69c68a5bfaac81909ce7f001dfb70c76 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1a780f88190abf11994e307b6ad |
completed | March 27, 2026, 9:07 p.m. |
| PD | Predicate disambiguation | batch_69c6f02ee3e08190a7a00c981129b22c |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:07 p.m.