Triple
T21661662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samir Geagea |
E534608
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Samir |
—
|
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 | Statement: [Samir Geagea, givenName, Samir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samir Context triple: [Samir Geagea, givenName, Samir]
-
A.
Samir
Samir is a fictional character portrayed by Indian actor Suraj Sharma, best known for his breakout role in the film "Life of Pi."
-
B.
Samir
chosen
Samir is a masculine given name of Arabic origin commonly used across the Middle East and other regions.
-
C.
Namir
Namir is a Hebrew surname most notably associated with Israeli politician and former Tel Aviv mayor Mordechai Namir.
-
D.
Nadim
Nadim is a masculine given name of Arabic origin commonly used in the Middle East and among Arabic-speaking communities worldwide.
-
E.
Samer
Samer is a small commune in northern France located within the Pas-de-Calais department.
- 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_69e0c467e1f48190af2650b19175abc4 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef6c0883d481908dfdc66832c34d74 |
completed | April 27, 2026, 2 p.m. |
Created at: April 16, 2026, 6:36 p.m.