Triple
T1496262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manayunk/Norristown Line |
E29692
|
entity |
| Predicate | hasRouteAlignment |
P28500
|
FINISHED |
| Object | along Schuylkill River corridor |
—
|
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: along Schuylkill River corridor | Statement: [Manayunk/Norristown Line, hasRouteAlignment, along Schuylkill River corridor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRouteAlignment Context triple: [Manayunk/Norristown Line, hasRouteAlignment, along Schuylkill River corridor]
-
A.
hasRouteDirection
Indicates that a specified route is associated with a particular travel direction (e.g., inbound, outbound, northbound).
-
B.
hasRoute
Indicates that there exists a path or connection enabling travel or communication from one entity to another.
-
C.
hasRouteType
Indicates that there is a specific kind or category of route associated with an entity (e.g., road, rail, bus line).
-
D.
hasRoadConfiguration
Indicates that there exists a specific arrangement or layout of roads associated with or characterizing an entity.
-
E.
hasApproachRoad
Indicates that one entity is connected to or accessed by another entity via an approach road leading to it.
- 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_69a498dba1d8819093b46a3a8d2485f1 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6ec70c48190a94f6e1002848eae |
completed | March 1, 2026, 11:08 p.m. |
| PD | Predicate disambiguation | batch_69a4c48a8cf48190a6ebf8d44a608a06 |
completed | March 1, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69a4c4feea448190b2b5071b28a5b608 |
completed | March 1, 2026, 11 p.m. |
Created at: March 1, 2026, 8:12 p.m.