Triple
T254314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metrobus (Washington, D.C.) |
E5403
|
entity |
| Predicate | hasColorAssociated |
P60
|
FINISHED |
| Object | red |
—
|
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: red | Statement: [Metrobus (Washington, D.C.), hasColorAssociated, red]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasColorAssociated Context triple: [Metrobus (Washington, D.C.), hasColorAssociated, red]
-
A.
hasLiturgicalColor
Indicates that something is associated with a specific liturgical color used in religious rites or ceremonies.
-
B.
colors
chosen
Indicates that one entity assigns, describes, or provides the color or colors of another entity.
-
C.
isAssociatedWith
Indicates that there exists a connection, relationship, or involvement between two entities without specifying its exact nature.
-
D.
hasLegendAssociatedWith
Indicates that something is connected to or accompanied by a traditional story, myth, or legend.
-
E.
hasFlowerColor
Indicates that an entity (typically a plant or flower) possesses a specific flower color.
- 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_69a2580a64ac8190ad76e34bb0715b5e |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25d548cac819081b1636b2a057c62 |
completed | Feb. 28, 2026, 3:13 a.m. |
| PD | Predicate disambiguation | batch_69a25b694c08819085bb4b256fa7736f |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.