Triple
T2822935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Medal of Honor (Japan) |
E54851
|
entity |
| Predicate | ribbonColorSystem |
P21498
|
FINISHED |
| Object | color of ribbon indicates category of merit |
—
|
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: color of ribbon indicates category of merit | Statement: [Medal of Honor (Japan), ribbonColorSystem, color of ribbon indicates category of merit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ribbonColorSystem Context triple: [Medal of Honor (Japan), ribbonColorSystem, color of ribbon indicates category of merit]
-
A.
ribbonColours
chosen
Indicates that there is an association between an entity and one or more colours of ribbons related to it.
-
B.
ribbonStripeColor
Indicates the color of a stripe on a ribbon in the relationship or depiction being described.
-
C.
berryColor
Indicates the color attribute associated with a berry.
-
D.
coneColor
Indicates that one entity is the color attribute assigned to a cone-shaped object.
-
E.
kitColorsHome
Indicates the colors used for a team's primary (home) kit or uniform.
- 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_69ab49e100c0819082a40cb797383243 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abdf15b7288190a03d1193cc0544a6 |
completed | March 7, 2026, 8:17 a.m. |
| PD | Predicate disambiguation | batch_69abdd08f2f481908c3da8a9c7a00552 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:59 p.m.