Triple
T16037694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Navid Kermani |
E389010
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Navid Kermani |
E389010
|
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: Navid Kermani | Statement: [Navid Kermani, name, Navid Kermani]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Navid Kermani Context triple: [Navid Kermani, name, Navid Kermani]
-
A.
Navid Kermani
chosen
Navid Kermani is a German writer, orientalist, and public intellectual known for his essays and novels exploring Islam, European culture, and intercultural dialogue.
-
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.
Kevin Nazemi
Kevin Nazemi is an American entrepreneur best known as a co-founder of the health insurance startup Oscar Health.
-
D.
Amir Naderi
Amir Naderi is an acclaimed Iranian filmmaker and screenwriter known for influential works in Iranian New Wave cinema and later international films.
-
E.
Ramin Kousha
Ramin Kousha is a film composer best known for scoring the cult disaster movie "Sharknado."
- 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_69d86dada3808190825d5f80d72fbe88 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1833da68881908710fb2c28e8c6d0 |
completed | April 17, 2026, 12:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffdbd5acb48190a10e40074fffd425 |
completed | May 10, 2026, 1:13 a.m. |
Created at: April 10, 2026, 4:56 a.m.