Triple
T21097031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fazlollah Zahedi |
E519794
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Zahedi
Zahedi is an Iranian surname most notably associated with General Fazlollah Zahedi, a key political and military figure in mid-20th-century Iran.
|
E1466378
|
NE FINISHED |
How this triple was built (4 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: Zahedi | Statement: [Fazlollah Zahedi, familyName, Zahedi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zahedi Context triple: [Fazlollah Zahedi, familyName, Zahedi]
-
A.
Nasiri
Nasiri is the family name of the ruling house of the Nasiri dynasty, a historical Muslim dynasty.
-
B.
Rūzbeh
Rūzbeh was the original Persian name of Ibn al-Muqaffaʿ, the influential 8th-century translator and prose stylist who helped shape early Arabic literature.
-
C.
Nematollah
Nematollah is a Persian given name commonly used in Iran and among Persian-speaking communities.
-
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.
Parviz
Parviz is a masculine given name of Persian origin commonly used in Iran and among Persian-speaking communities.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Zahedi Triple: [Fazlollah Zahedi, familyName, Zahedi]
Generated description
Zahedi is an Iranian surname most notably associated with General Fazlollah Zahedi, a key political and military figure in mid-20th-century Iran.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zahedi Target entity description: Zahedi is an Iranian surname most notably associated with General Fazlollah Zahedi, a key political and military figure in mid-20th-century Iran.
-
A.
Nasiri
Nasiri is the family name of the ruling house of the Nasiri dynasty, a historical Muslim dynasty.
-
B.
Rūzbeh
Rūzbeh was the original Persian name of Ibn al-Muqaffaʿ, the influential 8th-century translator and prose stylist who helped shape early Arabic literature.
-
C.
Nematollah
Nematollah is a Persian given name commonly used in Iran and among Persian-speaking communities.
-
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.
Parviz
Parviz is a masculine given name of Persian origin commonly used in Iran and among Persian-speaking communities.
- F. None of above. chosen
Provenance (5 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_69e0b508d8dc81909be940dafe36c8f7 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e71b595cdc8190ba7a6f3f71d40c3f |
completed | April 21, 2026, 6:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a095a60c2ec8190a7383948f0ff6cac |
completed | May 17, 2026, 6:04 a.m. |
| NEDg | Description generation | batch_6a095c017bec81909fd6c73a0fb677df |
completed | May 17, 2026, 6:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a095cee4c308190a08844f285231ff9 |
completed | May 17, 2026, 6:15 a.m. |
Created at: April 16, 2026, 2:52 p.m.