Triple
T8155580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niels Arestrup |
E190440
|
entity |
| Predicate | educatedAt |
P5
|
FINISHED |
| Object | Cours Simon |
E550556
|
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: Cours Simon | Statement: [Niels Arestrup, educatedAt, Cours Simon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cours Simon Context triple: [Niels Arestrup, educatedAt, Cours Simon]
-
A.
Cours Simon
chosen
Cours Simon is a renowned Parisian drama school known for training many prominent French actors.
-
B.
Simple Simon
Simple Simon is a 1930 Broadway musical comedy with music by Richard Rodgers and lyrics by Lorenz Hart.
-
C.
Simons
Simons is a Dutch surname most notably associated with Menno Simons, the 16th-century religious leader whose teachings gave rise to the Mennonite movement.
-
D.
Simons
Simons is a Canadian fashion and home goods department store chain known for its stylish, contemporary merchandise and distinctive store designs.
-
E.
Cours Saleya
Cours Saleya is a famous open-air market square in Nice, France, known for its vibrant flower, food, and antique markets surrounded by cafés and historic buildings.
- 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_69ca82bfeb6481909d07b91b5cf69f59 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb44d725b88190b77dc7537c1fa95d |
completed | March 31, 2026, 3:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbf0f68c88190be9aab03de6bf4a0 |
completed | April 1, 2026, 6:45 a.m. |
Created at: March 30, 2026, 5:37 p.m.