Triple
T9581934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mexico City Metrobús |
E231191
|
entity |
| Predicate | hasMapColorCoding |
P61608
|
FINISHED |
| Object | distinct color per line |
—
|
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: distinct color per line | Statement: [Mexico City Metrobús, hasMapColorCoding, distinct color per line]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMapColorCoding Context triple: [Mexico City Metrobús, hasMapColorCoding, distinct color per line]
-
A.
mapColor
Indicates a relationship where a map region or area is assigned or associated with a specific color, typically for visualization or categorization purposes.
-
B.
hasColorOnRouteMap
Indicates that a specific color is used to represent an entity (such as a route or line) on a route map.
-
C.
circuitColorOnMaps
Indicates that a circuit is represented with a specific color on one or more maps.
-
D.
mapColorLines
chosen
Indicates a relationship where colors are assigned or associated with specific lines, such as mapping each line to a particular color.
-
E.
hasColorSeriesConcept
Indicates that an entity is associated with a conceptual grouping or series defined by a particular color or set of colors.
- 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_69ca848161688190a68d514a0a9d5129 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99cd59008190888eb11f00f61994 |
completed | April 1, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69ccd59fd7408190b36831902e3f37f7 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:05 p.m.