Triple
T15506303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lyon Metro line A |
E379090
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Masséna station
Masséna station is an underground stop on Lyon's Metro system located in the 6th arrondissement, serving passengers on line A.
|
E1159992
|
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: Masséna station | Statement: [Lyon Metro line A, hasStation, Masséna station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Masséna station Context triple: [Lyon Metro line A, hasStation, Masséna station]
-
A.
Fabre station
Fabre station is a Montreal Metro station on the Blue Line serving the Rosemont–La Petite-Patrie borough.
-
B.
Mouton-Duvernet station
Mouton-Duvernet station is a Paris Métro station in the 14th arrondissement, serving Line 4 and named after French general Régis Barthélemy Mouton-Duvernet.
-
C.
Alésia station
Alésia station is a Paris Métro station on Line 4 located in the 14th arrondissement of Paris.
-
D.
Vavin station
Vavin station is a Paris Métro station serving the Montparnasse area on the Left Bank.
-
E.
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.
- 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: Masséna station Triple: [Lyon Metro line A, hasStation, Masséna station]
Generated description
Masséna station is an underground stop on Lyon's Metro system located in the 6th arrondissement, serving passengers on line A.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Masséna station Target entity description: Masséna station is an underground stop on Lyon's Metro system located in the 6th arrondissement, serving passengers on line A.
-
A.
Fabre station
Fabre station is a Montreal Metro station on the Blue Line serving the Rosemont–La Petite-Patrie borough.
-
B.
Mouton-Duvernet station
Mouton-Duvernet station is a Paris Métro station in the 14th arrondissement, serving Line 4 and named after French general Régis Barthélemy Mouton-Duvernet.
-
C.
Alésia station
Alésia station is a Paris Métro station on Line 4 located in the 14th arrondissement of Paris.
-
D.
Vavin station
Vavin station is a Paris Métro station serving the Montparnasse area on the Left Bank.
-
E.
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.
- 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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03fcea8888190a7b69aca360183c3 |
completed | April 16, 2026, 1:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff366bd31c81909e21075b6b601448 |
completed | May 9, 2026, 1:28 p.m. |
| NEDg | Description generation | batch_69ff371416e08190a84b2be7b7dbc4a3 |
completed | May 9, 2026, 1:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff379cc22881909e8a3e189d3f98d1 |
completed | May 9, 2026, 1:33 p.m. |
Created at: April 10, 2026, 3:55 a.m.