Triple
T2529149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pablo Picasso |
E56112
|
entity |
| Predicate | livedIn |
P75
|
FINISHED |
| Object | Vallauris |
E56112
|
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: Vallauris | Statement: [Pablo Picasso, livedIn, Vallauris]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vallauris Context triple: [Pablo Picasso, livedIn, Vallauris]
-
A.
Vallauris
chosen
Vallauris is a town in the French Riviera renowned for its pottery tradition and its association with Pablo Picasso, who lived and worked there for several years.
-
B.
Éveux
Éveux is a small commune in eastern France’s Rhône department, known for hosting Le Corbusier’s modernist monastery, the Couvent Sainte-Marie de La Tourette.
-
C.
Olbreuse
Olbreuse is a small locality in western France historically notable as the ancestral seat of the noble d’Olbreuse family.
-
D.
Laumière
Laumière is a Paris Métro station on the city’s northeastern side, located in the 19th arrondissement near the Canal de l’Ourcq.
-
E.
Clessé
Clessé is a wine-producing village in the Mâconnais region of Burgundy, France, known for its quality white wines.
- 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_69ab4a48e4f081908f1218d244608659 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd25903f08190b46e12d32278daca |
completed | March 7, 2026, 7:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28da24fe88190aabe2bcc520a35dc |
completed | March 12, 2026, 9:55 a.m. |
Created at: March 6, 2026, 9:46 p.m.