Triple
T11902099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A3 autoroute |
E283176
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Bagnolet |
E1087671
|
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: Bagnolet | Statement: [A3 autoroute, passesNear, Bagnolet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bagnolet Context triple: [A3 autoroute, passesNear, Bagnolet]
-
A.
Bagnolet
chosen
Bagnolet is a suburban commune in the eastern outskirts of Paris, France, known for its dense urban environment and major transport links including the Gallieni bus terminal.
-
B.
Levallois-Perret
Levallois-Perret is a densely populated suburban commune just northwest of central Paris, known for its residential character and proximity to the capital.
-
C.
Arcueil
Arcueil is a suburban commune in the southern outskirts of Paris, France, known historically as a residential area for notable scientists and intellectuals.
-
D.
Arcueil
Arcueil is a small river in France that serves as a tributary of the Alagnon.
-
E.
Montrouge
Montrouge is a suburban commune just south of Paris, France, known for its dense urban character and proximity to the 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_69d6ab2c07e88190ba13b0d21fd6cf33 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8dd1792648190853f15fbf217eebd |
completed | April 10, 2026, 11:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0170d8c9f0819099a398814f49f0ed |
completed | May 11, 2026, 6:02 a.m. |
Created at: April 8, 2026, 9:44 p.m.