Triple
T13707817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Secane, Pennsylvania |
E328687
|
entity |
| Predicate | hasRailStation |
P726
|
FINISHED |
| Object |
Secane station
Secane station is a commuter rail stop on SEPTA's Media/Wawa Line serving the community of Secane in Delaware County, Pennsylvania.
|
E1056422
|
NE FINISHED |
How this triple was built (4 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: Secane station | Statement: [Secane, Pennsylvania, hasRailStation, Secane station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Secane station Context triple: [Secane, Pennsylvania, hasRailStation, Secane station]
-
A.
Fabre station
Fabre station is a Montreal Metro station on the Blue Line serving the Rosemont–La Petite-Patrie borough.
-
B.
Alésia station
Alésia station is a Paris Métro station on Line 4 located in the 14th arrondissement of Paris.
-
C.
Lucien-L’Allier station
Lucien-L’Allier station is a downtown Montreal commuter rail and metro hub that serves as a key access point to the nearby Bell Centre and surrounding business district.
-
D.
Vavin station
Vavin station is a Paris Métro station serving the Montparnasse area on the Left Bank.
-
E.
Miromesnil station
Miromesnil station is a Paris Métro station in the 8th arrondissement, serving lines 9 and 13 near major boulevards and government buildings.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Secane station Triple: [Secane, Pennsylvania, hasRailStation, Secane station]
Generated description
Secane station is a commuter rail stop on SEPTA's Media/Wawa Line serving the community of Secane in Delaware County, Pennsylvania.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Secane station Target entity description: Secane station is a commuter rail stop on SEPTA's Media/Wawa Line serving the community of Secane in Delaware County, Pennsylvania.
-
A.
Fabre station
Fabre station is a Montreal Metro station on the Blue Line serving the Rosemont–La Petite-Patrie borough.
-
B.
Alésia station
Alésia station is a Paris Métro station on Line 4 located in the 14th arrondissement of Paris.
-
C.
Lucien-L’Allier station
Lucien-L’Allier station is a downtown Montreal commuter rail and metro hub that serves as a key access point to the nearby Bell Centre and surrounding business district.
-
D.
Vavin station
Vavin station is a Paris Métro station serving the Montparnasse area on the Left Bank.
-
E.
Miromesnil station
Miromesnil station is a Paris Métro station in the 8th arrondissement, serving lines 9 and 13 near major boulevards and government buildings.
- F. None of above. chosen
Provenance (5 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dcad18d48c8190a26be865c31975d6 |
completed | April 13, 2026, 8:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d52b3708190ae0945e65b271556 |
completed | May 3, 2026, 7:09 p.m. |
| NEDg | Description generation | batch_69f79df2984c8190bed380102ade0725 |
completed | May 3, 2026, 7:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f79e7d50a88190b2a90094dd6d48ad |
completed | May 3, 2026, 7:14 p.m. |
Created at: April 9, 2026, 9:54 p.m.