Triple
T11870567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mexico City Metro Line 4 |
E282395
|
entity |
| Predicate | hasMapColorCode |
P69958
|
FINISHED |
| Object | #00B2A9 |
—
|
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: #00B2A9 | Statement: [Mexico City Metro Line 4, hasMapColorCode, #00B2A9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMapColorCode Context triple: [Mexico City Metro Line 4, hasMapColorCode, #00B2A9]
-
A.
hasColorOnRouteMap
chosen
Indicates that a specific color is used to represent an entity (such as a route or line) on a route map.
-
B.
hasColorSymbol
Indicates that one entity is associated with another entity that serves as its representative or symbolic color.
-
C.
hasColorInfo
Indicates that an entity is associated with specific color-related information or attributes.
-
D.
hasColorType
Indicates that an entity is associated with a specific category or type of color.
-
E.
hasColorReference
Indicates that one entity serves as a reference or source for determining or specifying the color associated with another entity.
- 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_69d6ab2945d081908a5851c916cbcfb5 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8d39d2934819093b9f7006f45e5cb |
completed | April 10, 2026, 10:40 a.m. |
| PD | Predicate disambiguation | batch_69d8bb272f88819090c37c944c5a60ab |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:43 p.m.