Triple
T54392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SEPTA Market–Frankford Line |
E1071
|
entity |
| Predicate | hasSegmentType |
P2563
|
FINISHED |
| Object | subway |
—
|
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: subway | Statement: [SEPTA Market–Frankford Line, hasSegmentType, subway]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSegmentType Context triple: [SEPTA Market–Frankford Line, hasSegmentType, subway]
-
A.
hasStructureType
Indicates that an entity possesses or is classified by a specific structural type or configuration.
-
B.
hasMemberType
Indicates that an entity includes or is associated with members belonging to a specified type or category.
-
C.
hasTrailType
Indicates that an entity (such as a trail or route) is associated with a specific type or category of trail.
-
D.
hasNodeType
Indicates that an entity is associated with, or classified as, a specific type of node within a structured system or model.
-
E.
hasMaterialType
Indicates that something is composed of, made from, or characterized by a specific type of material.
- F. None of above. chosen
Provenance (4 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_69a248adc5b48190aa8db9fb092fb28a |
completed | Feb. 28, 2026, 1:45 a.m. |
| NER | Named-entity recognition | batch_69a24b3a9e848190b80de3c858678b3a |
completed | Feb. 28, 2026, 1:56 a.m. |
| PD | Predicate disambiguation | batch_69a24ac52fb08190aa7c38f83434f795 |
completed | Feb. 28, 2026, 1:54 a.m. |
| PDg | Predicate description generation | batch_69a24b39eef081909cec4333f2b25513 |
completed | Feb. 28, 2026, 1:56 a.m. |
Created at: Feb. 28, 2026, 1:50 a.m.