Triple
T36221868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R211 series |
E1047867
|
entity |
| Predicate | hasExteriorLivery |
P21786
|
FINISHED |
| Object | MTA New York City Subway standard 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: MTA New York City Subway standard livery | Statement: [R211 series, hasExteriorLivery, MTA New York City Subway standard livery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasExteriorLivery Context triple: [R211 series, hasExteriorLivery, MTA New York City Subway standard livery]
-
A.
hasLivery
chosen
Indicates that one entity bears or displays the distinctive colors, markings, or branding (livery) associated with another entity.
-
B.
liveryType
Indicates the specific style, design, or branding scheme applied to an entity’s exterior appearance.
-
C.
liveryFeature
Indicates a characteristic or design element that is part of a specific livery or external appearance scheme.
-
D.
liveryColors
Indicates the specific set of colors used as the official or characteristic color scheme associated with an entity (such as a brand, organization, or vehicle).
-
E.
liveryNumber
Indicates the identifying number assigned to a vehicle or asset within a particular livery or fleet.
- 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_69f76e42c878819095c8d19c0267fb87 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd3d46d1f48190a1b20dd063224b7d |
completed | May 8, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69fd3ae1510c81908fe1280efc17feee |
completed | May 8, 2026, 1:22 a.m. |
Created at: May 3, 2026, 4:09 p.m.