Triple
T8890102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sam Asghari |
E211640
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Sam Asghari |
E211640
|
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: Sam Asghari | Statement: [Sam Asghari, name, Sam Asghari]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sam Asghari Context triple: [Sam Asghari, name, Sam Asghari]
-
A.
Sam Asghari
chosen
Sam Asghari is an Iranian-American fitness trainer, model, and actor best known for his high-profile relationship and marriage to pop star Britney Spears.
-
B.
Navid Kermani
Navid Kermani is a German writer, orientalist, and public intellectual known for his essays and novels exploring Islam, European culture, and intercultural dialogue.
-
C.
Ramin Toloui
Ramin Toloui is an American economist and former global co-head of emerging markets at PIMCO who has served in senior international economic policy roles in the U.S. government.
-
D.
Darius Alizadeh
Darius Alizadeh is a character appearing in the James Bond continuation novel "Devil May Care" by Sebastian Faulks.
-
E.
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.
- 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_69ca83907954819096d52a245b635841 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc619188508190aacda410f0b4c98d |
completed | April 1, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfabf01d048190bed52b5b001d7ffa |
completed | April 3, 2026, noon |
Created at: March 30, 2026, 6:53 p.m.