Triple
T5828730
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Métro Line 9 |
E129292
|
entity |
| Predicate | station |
P726
|
FINISHED |
| Object | Strasbourg–Saint-Denis |
E202050
|
NE FINISHED |
How this triple was built (2 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: Strasbourg–Saint-Denis | Statement: [Paris Métro Line 9, station, Strasbourg–Saint-Denis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Strasbourg–Saint-Denis Context triple: [Paris Métro Line 9, station, Strasbourg–Saint-Denis]
-
A.
Strasbourg – Saint-Denis
chosen
Strasbourg – Saint-Denis is a major Paris Métro station and busy transport hub in central Paris, serving multiple lines near the historic Porte Saint-Denis.
-
B.
Beaugrenelle
Beaugrenelle is a modern riverside district in Paris known for its high-rise architecture, shopping center, and contemporary urban design along the Seine.
-
C.
Fleury-Mérogis
Fleury-Mérogis is a commune in the southern suburbs of Paris, France, best known for housing one of Europe’s largest prisons.
-
D.
Saint-Denis
Saint-Denis is the largest city and administrative, economic, and cultural center of the French overseas department of Réunion in the Indian Ocean.
-
E.
Saint-Denis
Saint-Denis is a northern suburb of Paris known for its historic basilica, diverse population, and major sports venues including the Stade de France.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c00849d55481908b4f9f5543e0bf6d |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03467dfe48190b51757b33681bc20 |
completed | March 22, 2026, 6:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a18c48588190b4848dff1c277079 |
completed | March 23, 2026, 2:12 a.m. |
Created at: March 22, 2026, 3:53 p.m.