Triple
T8791797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean |
E209182
|
entity |
| Predicate | exampleCompoundName |
P85415
|
FINISHED |
| Object | Jean-Michel |
E226333
|
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: Jean-Michel | Statement: [Jean, exampleCompoundName, Jean-Michel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jean-Michel Context triple: [Jean, exampleCompoundName, Jean-Michel]
-
A.
Jean-Michel
chosen
Jean-Michel is the given name of the influential American artist Jean-Michel Basquiat, a leading figure in 1980s neo-expressionist painting.
-
B.
Jean-Michel Bernard
Jean-Michel Bernard is a French composer and pianist known for his film scores and collaborations with director Michel Gondry.
-
C.
Michel
Michel is a fictional character appearing in Frederick Forsyth’s political thriller novel "The Dogs of War."
-
D.
Michel
Michel is the birth name of the acclaimed Egyptian actor Omar Sharif, renowned for his roles in classic films such as "Lawrence of Arabia" and "Doctor Zhivago."
-
E.
Michel
Michel is a French given name commonly used for males, equivalent to "Michael" in English.
- 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_69ca836240888190a62b262e56a69d2f |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f8e6e4881909155c40c52bc082c |
completed | March 31, 2026, 11:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf890b49048190b49c784f84e23496 |
completed | April 3, 2026, 9:31 a.m. |
Created at: March 30, 2026, 6:43 p.m.