Triple
T7374991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zapruder film |
E170100
|
entity |
| Predicate | shotFrom |
P76638
|
FINISHED |
| Object | concrete pedestal along Elm Street in Dealey Plaza |
—
|
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: concrete pedestal along Elm Street in Dealey Plaza | Statement: [Zapruder film, shotFrom, concrete pedestal along Elm Street in Dealey Plaza]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shotFrom Context triple: [Zapruder film, shotFrom, concrete pedestal along Elm Street in Dealey Plaza]
-
A.
shotOn
Indicates that one entity fired or took a shot at another entity, typically in a sports or combat context.
-
B.
shoots
Indicates that one entity propels a projectile or discharge toward another entity, typically with the intent to hit or affect it.
-
C.
shotType
Indicates the specific kind or category of shot used or taken in a given context (e.g., in film, photography, or sports).
-
D.
shootsSide
Indicates that one entity fires a projectile or weapon toward the side of another entity, rather than directly at its front or back.
-
E.
shootsCatches
Indicates that one entity shoots something that is then caught by another entity.
- 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_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. |
| PDg | Predicate description generation | batch_69c6f0ebf9c88190af5a4d87d3fd338a |
completed | March 27, 2026, 9:04 p.m. |
Created at: March 27, 2026, 3:07 p.m.