Triple
T23092088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daoud |
E575780
|
entity |
| Predicate | isMiddleNameOf |
P143
|
FINISHED |
| Object |
Daoud Dean
Daoud Dean is an individual whose middle name is Daoud, suggesting a personal name likely of Arabic origin.
|
E1570366
|
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: Daoud Dean | Statement: [Daoud, isMiddleNameOf, Daoud Dean]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daoud Dean Context triple: [Daoud, isMiddleNameOf, Daoud Dean]
-
A.
Omar Abdulrahman
Omar Abdulrahman is an Emirati attacking midfielder renowned for his playmaking skills and creativity, widely regarded as one of the most talented footballers in the history of UAE football.
-
B.
Khalid Mohamed
Khalid Mohamed is an Indian film critic, journalist, and filmmaker known for his work on several notable Hindi films as a writer and director.
-
C.
Ghaith Abdul-Ahad
Ghaith Abdul-Ahad is an Iraqi journalist and war correspondent renowned for his frontline reporting from conflict zones across the Middle East.
-
D.
Omar Hassan-Reep
Omar Hassan-Reep is a film editor known for his work on the 2010 horror movie "Chain Letter."
-
E.
Nidal Ayyad
Nidal Ayyad is a Kuwaiti-born chemical engineer and Islamist militant who was convicted for his role in planning and carrying out the 1993 World Trade Center bombing in New York City.
- 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: Daoud Dean Triple: [Daoud, isMiddleNameOf, Daoud Dean]
Generated description
Daoud Dean is an individual whose middle name is Daoud, suggesting a personal name likely of Arabic origin.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daoud Dean Target entity description: Daoud Dean is an individual whose middle name is Daoud, suggesting a personal name likely of Arabic origin.
-
A.
Omar Abdulrahman
Omar Abdulrahman is an Emirati attacking midfielder renowned for his playmaking skills and creativity, widely regarded as one of the most talented footballers in the history of UAE football.
-
B.
Khalid Mohamed
Khalid Mohamed is an Indian film critic, journalist, and filmmaker known for his work on several notable Hindi films as a writer and director.
-
C.
Ghaith Abdul-Ahad
Ghaith Abdul-Ahad is an Iraqi journalist and war correspondent renowned for his frontline reporting from conflict zones across the Middle East.
-
D.
Omar Hassan-Reep
Omar Hassan-Reep is a film editor known for his work on the 2010 horror movie "Chain Letter."
-
E.
Nidal Ayyad
Nidal Ayyad is a Kuwaiti-born chemical engineer and Islamist militant who was convicted for his role in planning and carrying out the 1993 World Trade Center bombing in New York City.
- 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_69e245bf3e3c819086d3448720efc01b |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18daa68d48190842e9cb6de31ea79 |
completed | April 29, 2026, 4:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c15b97b6881909cbc1afc68ea80b6 |
completed | May 19, 2026, 7:48 a.m. |
| NEDg | Description generation | batch_6a0c175e246081909d6451f245ccfe66 |
completed | May 19, 2026, 7:55 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c1820a4d88190aed59f50b4e7ec47 |
completed | May 19, 2026, 7:58 a.m. |
Created at: April 17, 2026, 3:57 p.m.