Triple
T16430157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yannick |
E399050
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Yann |
E26462
|
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: Yann | Statement: [Yannick, relatedName, Yann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yann Context triple: [Yannick, relatedName, Yann]
-
A.
Yann
chosen
Yann is the given name of Yann LeCun, a pioneering computer scientist known for his foundational work in deep learning and convolutional neural networks.
-
B.
Yann Weymouth
Yann Weymouth is an American architect known for designing prominent cultural and museum buildings, including the Salvador Dalí Museum in St. Petersburg, Florida.
-
C.
Yves
Yves is a masculine given name of French origin commonly used in Francophone countries.
-
D.
Yannick
Yannick is a masculine given name of Breton origin, commonly used in French-speaking regions and borne by figures such as conductor Yannick Nézet-Séguin.
-
E.
Sylvain
Sylvain is a masculine given name of French origin commonly used in Francophone countries.
- 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e328fe0f488190ac34aa677c980a20 |
completed | April 18, 2026, 6:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f477674819093bcf9f0df43ebf9 |
completed | May 10, 2026, 9:26 a.m. |
Created at: April 10, 2026, 5:10 a.m.