Triple
T18419092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samir Bannout |
E441973
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Samir Bannout |
—
|
NE NERFINISHED |
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: Samir Bannout | Statement: [Samir Bannout, name, Samir Bannout]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samir Bannout Context triple: [Samir Bannout, name, Samir Bannout]
-
A.
Samir Bannout
chosen
Samir Bannout is a Lebanese-American professional bodybuilder best known for winning the 1983 Mr. Olympia title and for his exceptionally detailed back development.
-
B.
Samir Kassis
Samir Kassis is a notable individual recognized as a prominent bearer of the surname Kassis.
-
C.
Samir Hammoud
Samir Hammoud is a person notable enough to be specifically identified as a bearer of the surname Hammoud.
-
D.
Nabil Hammoud
Nabil Hammoud is an individual recognized as a notable bearer of the surname Hammoud.
-
E.
Anthony Ghannam
Anthony Ghannam is an American voice actor best known for his role in the animated film "Bambi II."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9eb8a508190a942fd75ebd8b1dc |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e51a2a0fb08190b409ed200a9d86a6 |
completed | April 19, 2026, 6:08 p.m. |
Created at: April 10, 2026, 10:47 a.m.