Triple
T20664934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toulouse Metro Line A |
E507858
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Esquirol
Esquirol is a metro station on Toulouse's Line A located near the historic city center and the Place Esquirol.
|
E1443816
|
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: Esquirol | Statement: [Toulouse Metro Line A, hasStation, Esquirol]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Esquirol Context triple: [Toulouse Metro Line A, hasStation, Esquirol]
-
A.
Guerin
Guerin is a surname of French origin borne by various notable individuals across fields such as arts, sports, and public life.
-
B.
Lebrun
Lebrun is a French surname borne by various notable figures in politics, arts, and other fields.
-
C.
Béraud
Béraud is a French surname most notably associated with the 19th-century painter Jean Béraud, renowned for his vivid depictions of Parisian life during the Belle Époque.
-
D.
Réclère
Réclère is a former Swiss municipality in the canton of Jura that was incorporated into the new municipality of Haute-Ajoie.
-
E.
Greuze
Greuze is a French surname most famously associated with Jean-Baptiste Greuze, an 18th-century painter known for his sentimental and moralizing genre scenes.
- 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: Esquirol Triple: [Toulouse Metro Line A, hasStation, Esquirol]
Generated description
Esquirol is a metro station on Toulouse's Line A located near the historic city center and the Place Esquirol.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Esquirol Target entity description: Esquirol is a metro station on Toulouse's Line A located near the historic city center and the Place Esquirol.
-
A.
Guerin
Guerin is a surname of French origin borne by various notable individuals across fields such as arts, sports, and public life.
-
B.
Lebrun
Lebrun is a French surname borne by various notable figures in politics, arts, and other fields.
-
C.
Béraud
Béraud is a French surname most notably associated with the 19th-century painter Jean Béraud, renowned for his vivid depictions of Parisian life during the Belle Époque.
-
D.
Réclère
Réclère is a former Swiss municipality in the canton of Jura that was incorporated into the new municipality of Haute-Ajoie.
-
E.
Greuze
Greuze is a French surname most famously associated with Jean-Baptiste Greuze, an 18th-century painter known for his sentimental and moralizing genre scenes.
- 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.