Triple
T6219290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1st arrondissement of Paris |
E139069
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Place Dauphine |
E221442
|
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: Place Dauphine | Statement: [1st arrondissement of Paris, contains, Place Dauphine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Place Dauphine Context triple: [1st arrondissement of Paris, contains, Place Dauphine]
-
A.
Place Dauphine
chosen
Place Dauphine is a historic, triangular public square in central Paris, known for its quiet charm and classical architecture near the western end of the Île de la Cité.
-
B.
Place du Château
Place du Château is a historic square in Strasbourg, France, known for its views of Strasbourg Cathedral and its surrounding architectural landmarks.
-
C.
Place du Bourg-de-Four
Place du Bourg-de-Four is a historic central square in Geneva’s Old Town, known for its cafés, shops, and role as a traditional meeting place.
-
D.
Porte Dauphine
Porte Dauphine is a Paris Métro station on Line 2, located near the Bois de Boulogne in the 16th arrondissement of Paris.
-
E.
Billancourt
Billancourt is a Paris Métro station in Boulogne-Billancourt serving the western suburbs of the French capital.
- 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_69c008aecb0c81909984b48f733ce8ae |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062bbb768819099402d367f124639 |
completed | March 22, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c20dbbacf08190bbbb2863e19c3e7c |
completed | March 24, 2026, 4:06 a.m. |
Created at: March 22, 2026, 4:21 p.m.