Triple
T8883716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 4 (Paris Métro) |
E211472
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Saint-Michel |
E207643
|
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: Saint-Michel | Statement: [Line 4 (Paris Métro), hasStation, Saint-Michel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saint-Michel Context triple: [Line 4 (Paris Métro), hasStation, Saint-Michel]
-
A.
Saint-Michel
Saint-Michel is a Montreal Metro station that serves as the eastern terminus of the Blue Line in Montreal, Quebec, Canada.
-
B.
Saint-Michel
chosen
Saint-Michel is a central Paris Métro station on the Left Bank, serving the busy Saint-Michel–Notre-Dame area near the Seine and key historic landmarks.
-
C.
Saint-Paul-Saint-Louis
Saint-Paul-Saint-Louis is a historic 17th-century Roman Catholic church in Paris’s Marais district, noted for its Baroque architecture and richly decorated interior.
-
D.
Saint-Denis-du-Port
Saint-Denis-du-Port is a locality in France historically noted as the place where the 18th-century French painter and etcher Jean-Baptiste Le Prince died.
-
E.
Saint-Georges
Saint-Georges is a city in the Beauce region of Quebec, Canada, known as a regional economic center with a strong manufacturing base and scenic riverside setting.
- 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_69ca838f9e20819096ab1f236a70381a |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc616b2d988190b923ef1e33aab787 |
completed | April 1, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfabd254148190b5ea3d308fe96851 |
completed | April 3, 2026, noon |
Created at: March 30, 2026, 6:53 p.m.