Triple
T9732246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Métro Line 3 |
E235772
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Bourse
Bourse is a Paris Métro station in the 2nd arrondissement, named after and serving the historic Paris stock exchange area.
|
E816339
|
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: Bourse | Statement: [Paris Métro Line 3, hasStation, Bourse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bourse Context triple: [Paris Métro Line 3, hasStation, Bourse]
-
A.
Borsa
Borsa is the nickname of Roger Borsa, a medieval Norman nobleman who ruled as Duke of Apulia in southern Italy.
-
B.
Bourse de Paris
Bourse de Paris is the historic Parisian stock exchange, long a central hub of French and European financial trading.
-
C.
Vieille Bourse
Vieille Bourse is a 17th-century former stock exchange in Lille, France, renowned for its ornate Flemish Renaissance architecture and central courtyard now used for book markets and cultural events.
-
D.
Wall Street
Wall Street is the historic financial district in Lower Manhattan that serves as a global center for banking, trading, and economic power.
-
E.
Wall Street
Wall Street is a 1987 American drama film directed by Oliver Stone that explores the high-stakes world of corporate finance and greed in New York City.
- 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: Bourse Triple: [Paris Métro Line 3, hasStation, Bourse]
Generated description
Bourse is a Paris Métro station in the 2nd arrondissement, named after and serving the historic Paris stock exchange area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bourse Target entity description: Bourse is a Paris Métro station in the 2nd arrondissement, named after and serving the historic Paris stock exchange area.
-
A.
Borsa
Borsa is the nickname of Roger Borsa, a medieval Norman nobleman who ruled as Duke of Apulia in southern Italy.
-
B.
Bourse de Paris
Bourse de Paris is the historic Parisian stock exchange, long a central hub of French and European financial trading.
-
C.
Vieille Bourse
Vieille Bourse is a 17th-century former stock exchange in Lille, France, renowned for its ornate Flemish Renaissance architecture and central courtyard now used for book markets and cultural events.
-
D.
Wall Street
Wall Street is a 1987 American drama film directed by Oliver Stone that explores the high-stakes world of corporate finance and greed in New York City.
-
E.
Wall Street
Wall Street is the historic financial district in Lower Manhattan that serves as a global center for banking, trading, and economic power.
- 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_69ca84d0fad481909cdd45aa77416c48 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9eb3d6e4819090b3c7fb92550c57 |
completed | April 1, 2026, 10:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d19fbbba2081909a15725a68423162 |
completed | April 4, 2026, 11:33 p.m. |
| NEDg | Description generation | batch_69d1a065ce008190985b792302daa7cb |
completed | April 4, 2026, 11:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1a0f811fc8190b6a46a0441159089 |
completed | April 4, 2026, 11:38 p.m. |
Created at: March 30, 2026, 8:22 p.m.