Triple
T8449643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Orange |
E199768
|
entity |
| Predicate | shotBy |
P83427
|
FINISHED |
| Object | Mr. Blonde’s victim’s getaway driver |
—
|
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: Mr. Blonde’s victim’s getaway driver | Statement: [Mr. Orange, shotBy, Mr. Blonde’s victim’s getaway driver]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shotBy Context triple: [Mr. Orange, shotBy, Mr. Blonde’s victim’s getaway driver]
-
A.
shotOn
Indicates that one entity fired or took a shot at another entity, typically in a sports or combat context.
-
B.
shotFrom
Indicates that something is propelled or discharged starting at a particular source or origin.
-
C.
shoots
Indicates that one entity propels a projectile or discharge toward another entity, typically with the intent to hit or affect it.
-
D.
shotType
Indicates the specific kind or category of shot used or taken in a given context (e.g., in film, photography, or sports).
-
E.
shootsSide
Indicates that one entity fires a projectile or weapon toward the side of another entity, rather than directly at its front or back.
- 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_69ca83170f9081909cd98f55614c6476 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe44707b88190b3d8b30c45ef4496 |
completed | March 31, 2026, 3:12 p.m. |
| PD | Predicate disambiguation | batch_69cbd0f5a3648190beb53a139a2d5482 |
completed | March 31, 2026, 1:49 p.m. |
| PDg | Predicate description generation | batch_69cbe30c2d088190b4cb89adb4e88273 |
completed | March 31, 2026, 3:06 p.m. |
Created at: March 30, 2026, 6:09 p.m.