Triple
T6985098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marseille campus |
E161940
|
entity |
| Predicate | servesCity |
P82
|
FINISHED |
| Object | Marseille |
E15143
|
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: Marseille | Statement: [Marseille campus, servesCity, Marseille]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marseille Context triple: [Marseille campus, servesCity, Marseille]
-
A.
Marseille
chosen
Marseille is a historic Mediterranean port city in southern France known for its diverse culture, maritime heritage, and role as a major economic hub.
-
B.
Lyon
Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
-
C.
Toulon
Toulon is a major port city on France’s Mediterranean coast that serves as the principal base of the French Navy.
-
D.
Aix-en-Provence
Aix-en-Provence is a historic and picturesque city in southern France, renowned for its Provençal charm, fountains, and as the hometown of painter Paul Cézanne.
-
E.
Montpellier
Montpellier is a major city in southern France known for its medieval old town, vibrant university life, and proximity to the Mediterranean coast.
- 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_69c68855dc0481909b4c7e9e9ed273db |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db91fbc881908c26b7b991995062 |
completed | March 27, 2026, 7:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7882712548190a0c7e5660c61625d |
completed | March 28, 2026, 7:49 a.m. |
Created at: March 27, 2026, 2:31 p.m.