Triple
T34621083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EWS |
E889003
|
entity |
| Predicate | locomotiveLivery |
P42927
|
FINISHED |
| Object | maroon and gold |
—
|
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: maroon and gold | Statement: [EWS, locomotiveLivery, maroon and gold]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locomotiveLivery Context triple: [EWS, locomotiveLivery, maroon and gold]
-
A.
locomotiveWorks
Indicates a relationship where an entity is a facility or company that builds, repairs, or maintains locomotives.
-
B.
liveryFeature
Indicates a characteristic or design element that is part of a specific livery or external appearance scheme.
-
C.
locomotiveSeries
Indicates that one locomotive belongs to, or is classified under, a particular locomotive series or model line.
-
D.
liveryColors
chosen
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.
locomotiveName
Indicates that a locomotive has a specific name assigned to it.
- 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_69f349d584e08190b40b9f6281ad50c4 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7234bcaa48190ac970759d34e254a |
completed | May 3, 2026, 10:28 a.m. |
| PD | Predicate disambiguation | batch_69f72155c48881909bd40b9aa3febd5a |
completed | May 3, 2026, 10:20 a.m. |
Created at: May 1, 2026, 2:04 a.m.