Triple
T1617606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sikorsky VH-60N White Hawk |
E34754
|
entity |
| Predicate | paintScheme |
P29272
|
FINISHED |
| Object | green and white presidential livery |
—
|
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: green and white presidential livery | Statement: [Sikorsky VH-60N White Hawk, paintScheme, green and white presidential livery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: paintScheme Context triple: [Sikorsky VH-60N White Hawk, paintScheme, green and white presidential livery]
-
A.
hasColourScheme
chosen
Indicates that an entity is associated with a particular set or pattern of colors used in its design or appearance.
-
B.
colors
Indicates that one entity assigns, describes, or provides the color or colors of another entity.
-
C.
paintScheme_Operation Crossroads
Indicates a relationship where an entity’s paint scheme is specifically associated with Operation Crossroads.
-
D.
mapColor
Indicates a relationship where a map region or area is assigned or associated with a specific color, typically for visualization or categorization purposes.
-
E.
colorCharge
Indicates a relationship where an entity possesses a specific quantum color charge (such as red, green, or blue) in the context of strong nuclear interactions.
- 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_69a885ffc5ec819091afa325d5f9611c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a93fef600c819080fe75c42c8e6dac |
completed | March 5, 2026, 8:33 a.m. |
| PD | Predicate disambiguation | batch_69a907c52a548190b648a31ea306dd5b |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.