Triple
T4228794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Staffordshire Railway |
E94525
|
entity |
| Predicate | hadLiveryColor |
P42927
|
FINISHED |
| Object | crimson lake |
—
|
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: crimson lake | Statement: [North Staffordshire Railway, hadLiveryColor, crimson lake]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadLiveryColor Context triple: [North Staffordshire Railway, hadLiveryColor, crimson lake]
-
A.
hasLivery
Indicates that one entity bears or displays the distinctive colors, markings, or branding (livery) associated with another entity.
-
B.
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).
-
C.
liveryFeature
Indicates a characteristic or design element that is part of a specific livery or external appearance scheme.
-
D.
helmetColor
Indicates the specific color attribute assigned to a helmet in the relationship.
-
E.
capeColor
Indicates the color attribute associated with a cape worn or possessed by an 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_69b3453700a08190ae88792e3dc63207 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34e51817c8190bff50f2c3b5deea0 |
completed | March 12, 2026, 11:37 p.m. |
| PD | Predicate disambiguation | batch_69b347f1d7b48190bd8974c03c7dc937 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:04 p.m.