Triple
T276169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claude Monet |
E5253
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Oscar |
E11086
|
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: Oscar | Statement: [Claude Monet, givenName, Oscar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oscar Context triple: [Claude Monet, givenName, Oscar]
-
A.
Oscar
chosen
The Oscar is a prestigious film industry award presented annually by the Academy of Motion Picture Arts and Sciences to honor outstanding cinematic achievements.
-
B.
Emmy
Emmy is the affectionate nickname of Emmy Noether, the pioneering German mathematician renowned for her groundbreaking contributions to abstract algebra and theoretical physics.
-
C.
Leslie
Leslie is a small town in Fife, Scotland, situated near Glenrothes and known historically for its textile and papermaking industries.
-
D.
Oskar
Oskar is a masculine given name of Germanic origin, commonly used in various European countries.
-
E.
Garland
Garland is a large suburban city in the Dallas–Fort Worth metropolitan area known for its diverse community and mixed residential, commercial, and industrial character.
- 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_69a257e6c8788190987dfe705ca2912a |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25dec53ac8190912f3d79576131fa |
completed | Feb. 28, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a391525a7081909166884a8ea47ace |
completed | March 1, 2026, 1:07 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.