Triple
T21296923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ali Mosaffa |
E524947
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object | Mani Mosaffa |
—
|
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: Mani Mosaffa | Statement: [Ali Mosaffa, hasChild, Mani Mosaffa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mani Mosaffa Context triple: [Ali Mosaffa, hasChild, Mani Mosaffa]
-
A.
Mani Mosaffa
chosen
Mani Mosaffa is the son of acclaimed Iranian actress Leila Hatami and director Ali Mosaffa, belonging to a prominent family in Iranian cinema.
-
B.
Mohsen
Mohsen is a masculine given name of Persian and Arabic origin, commonly used in Iran and other Muslim-majority countries.
-
C.
Mir-Hossein
Mir-Hossein is the given name of Mir-Hossein Mousavi, an Iranian reformist politician and former Prime Minister of Iran.
-
D.
Al Yeganeh
Al Yeganeh is a New York City soup vendor and restaurateur whose strict, idiosyncratic service style inspired the famous "Soup Nazi" character on the television show Seinfeld.
-
E.
Manal al-Sharif
Manal al-Sharif is a Saudi women’s rights activist best known for leading the women’s driving campaign in Saudi Arabia and challenging the kingdom’s male guardianship system.
- 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_69e0b517e6748190850d6f6ddf323d69 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7385968308190bc9fe5c2bd4598e6 |
completed | April 21, 2026, 8:42 a.m. |
Created at: April 16, 2026, 4:04 p.m.