Triple
T16066572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Omer |
E389746
|
entity |
| Predicate | hasNotableBearerExample |
P458
|
FINISHED |
| Object | Omer Asik |
E1121758
|
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: Omer Asik | Statement: [Omer, hasNotableBearerExample, Omer Asik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Omer Asik Context triple: [Omer, hasNotableBearerExample, Omer Asik]
-
A.
Hedo Türkoğlu
Hedo Türkoğlu is a retired Turkish professional basketball player best known for his NBA career and as one of Turkey’s most prominent international basketball stars.
-
B.
Ersan İlyasova
Ersan İlyasova is a Turkish professional basketball player known for his versatile forward play and successful NBA career with multiple teams.
-
C.
Hamed Haddadi
Hamed Haddadi is an Iranian professional basketball player best known as the first Iranian to play in the NBA, primarily as a center for the Memphis Grizzlies.
-
D.
Ömer Aşık
chosen
Ömer Aşık is a Turkish professional basketball center known for his defensive presence and rebounding in the NBA and international play.
-
E.
Caglar Gulcehre
Caglar Gulcehre is a machine learning researcher known for his contributions to neural network-based natural language processing and sequence modeling, including work on RNN encoder–decoder architectures for machine translation.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1837ca628819081dfc439fe322d58 |
completed | April 17, 2026, 12:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe480d59c8190962ac596a872b5e0 |
completed | May 10, 2026, 1:50 a.m. |
Created at: April 10, 2026, 4:57 a.m.