Triple
T3046641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uptown (NYC Subway Direction) |
E83465
|
entity |
| Predicate | appliesToLineType |
P15027
|
FINISHED |
| Object | numbered subway lines |
—
|
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: numbered subway lines | Statement: [Uptown (NYC Subway Direction), appliesToLineType, numbered subway lines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesToLineType Context triple: [Uptown (NYC Subway Direction), appliesToLineType, numbered subway lines]
-
A.
lineType
chosen
Indicates the specific category or style of a line used in a representation, such as its function or visual convention.
-
B.
appliesTo
Indicates that something is relevant, valid, or has effect in relation to a particular entity, case, or context.
-
C.
isLinedWith
Indicates that one object or surface is covered, edged, or internally coated along its length or area with another material or layer.
-
D.
appliesToTransitLine
Indicates that a rule, condition, or characteristic is specifically associated with and relevant to a particular transit line.
-
E.
appliesToPropertyType
Indicates that something (such as a rule, constraint, or operation) is relevant to or valid for a specific type of property.
- 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_69ad8b24924c8190a9bb6f61d519e4ae |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9bad11b48190a5bd01e91a320e14 |
completed | March 8, 2026, 3:54 p.m. |
| PD | Predicate disambiguation | batch_69ad961fc62c819087c4c3a44b00847d |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3:01 p.m.