Triple
T20664931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toulouse Metro Line A |
E507858
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Arènes
Arènes is a metro station in Toulouse, France, serving as an interchange point between the Toulouse Metro, tram, and bus networks.
|
E1443815
|
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: Arènes | Statement: [Toulouse Metro Line A, hasStation, Arènes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arènes Context triple: [Toulouse Metro Line A, hasStation, Arènes]
-
A.
Hadria
Hadria is a feminine given name of Latin origin, closely related to Adriaan/Adrian and historically linked to the ancient town of Hadria in Italy.
-
B.
Paradou
Paradou is a small picturesque commune in southern France’s Provence region, known for its traditional stone houses and proximity to the Alpilles hills.
-
C.
Cité
Cité is a Paris Métro station located on the Île de la Cité in the historic center of Paris.
-
D.
Lapalisse
Lapalisse is a small historic town in central France, known for its medieval château and its association with the nobleman Jacques de La Palice.
-
E.
Hoschedé
Hoschedé is a French surname notably associated with the family closely linked to Impressionist painter Claude Monet.
- 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: Arènes Triple: [Toulouse Metro Line A, hasStation, Arènes]
Generated description
Arènes is a metro station in Toulouse, France, serving as an interchange point between the Toulouse Metro, tram, and bus networks.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Arènes Target entity description: Arènes is a metro station in Toulouse, France, serving as an interchange point between the Toulouse Metro, tram, and bus networks.
-
A.
Hadria
Hadria is a feminine given name of Latin origin, closely related to Adriaan/Adrian and historically linked to the ancient town of Hadria in Italy.
-
B.
Paradou
Paradou is a small picturesque commune in southern France’s Provence region, known for its traditional stone houses and proximity to the Alpilles hills.
-
C.
Cité
Cité is a Paris Métro station located on the Île de la Cité in the historic center of Paris.
-
D.
Lapalisse
Lapalisse is a small historic town in central France, known for its medieval château and its association with the nobleman Jacques de La Palice.
-
E.
Hoschedé
Hoschedé is a French surname notably associated with the family closely linked to Impressionist painter Claude Monet.
- 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_69e0b4c059bc81908ea762cd73ea4424 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b5c2c6d48190bbfe505cf7d973f9 |
completed | April 20, 2026, 11:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08cd5d0da481909eebd69b8b314503 |
completed | May 16, 2026, 8:02 p.m. |
| NEDg | Description generation | batch_6a08d175206c8190b119bb1a2d06462f |
completed | May 16, 2026, 8:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08d28e02fc8190ab694673156ab0c0 |
completed | May 16, 2026, 8:24 p.m. |
Created at: April 16, 2026, 11:44 a.m.