Triple
T8866341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jules Dalou |
E211026
|
entity |
| Predicate | educatedAt |
P5
|
FINISHED |
| Object | Petite École |
E326690
|
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: Petite École | Statement: [Jules Dalou, educatedAt, Petite École]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Petite École Context triple: [Jules Dalou, educatedAt, Petite École]
-
A.
Petite École
chosen
Petite École was a French art school in Paris known for training young artists in drawing and decorative arts, attended by notable figures such as Auguste Rodin.
-
B.
École de la Gradelle
École de la Gradelle is a local primary school serving the community of Chêne-Bougeries in the canton of Geneva, Switzerland.
-
C.
Collège de Coqueret
Collège de Coqueret was a notable 16th-century Parisian humanist college renowned for educating prominent French Renaissance figures such as poet Pierre de Ronsard.
-
D.
Collège Sévigné
Collège Sévigné is a private, progressive secondary school in Paris known for its strong academic tradition and notable alumni.
-
E.
École Alsacienne
École Alsacienne is a prestigious private school in Paris known for its rigorous academics and progressive educational approach.
- 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_69ca838d3c7c8190a849566d5afd2b11 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6106d41081909db710bda8aaf6fd |
completed | April 1, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfa0dafdf48190abc1fe1a8397e339 |
completed | April 3, 2026, 11:13 a.m. |
Created at: March 30, 2026, 6:51 p.m.