Triple
T15623771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zekeria Ebrahimi |
E375628
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Zekeria Ebrahimi |
E375628
|
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: Zekeria Ebrahimi | Statement: [Zekeria Ebrahimi, name, Zekeria Ebrahimi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zekeria Ebrahimi Context triple: [Zekeria Ebrahimi, name, Zekeria Ebrahimi]
-
A.
Zekeria Ebrahimi
chosen
Zekeria Ebrahimi is an Afghan actor best known for his role as the young Amir in the film adaptation of "The Kite Runner."
-
B.
Mehdi Hatamian
Mehdi Hatamian is an electrical engineer and technologist recognized for his influential contributions to high-speed integrated circuits and signal processing, for which he has received major industry honors.
-
C.
Amir Esmailian
Amir Esmailian is a Canadian music executive and talent manager best known for co-founding XO and helping develop the career of The Weeknd.
-
D.
Darius Alizadeh
Darius Alizadeh is a character appearing in the James Bond continuation novel "Devil May Care" by Sebastian Faulks.
-
E.
Behzad Farahani
Behzad Farahani is an Iranian actor and playwright known for his significant contributions to Iranian theater and cinema.
- 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_69d85ccf2794819096cda4cbcb02d478 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e9cfd94819091459aa17a002eaf |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff6ece07608190a705f108c8c2979a |
completed | May 9, 2026, 5:28 p.m. |
Created at: April 10, 2026, 4:14 a.m.