Triple
T1158774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faten Hamama |
E24444
|
entity |
| Predicate | workedWith |
P398
|
FINISHED |
| Object |
Henry Barakat
Henry Barakat was a prominent Egyptian film director and one of the leading figures of classical Egyptian cinema.
|
E137048
|
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: Henry Barakat | Statement: [Faten Hamama, workedWith, Henry Barakat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Henry Barakat Context triple: [Faten Hamama, workedWith, Henry Barakat]
-
A.
Issa Kassis
Issa Kassis is a Palestinian politician who serves as the mayor of Ramallah, a major cultural and administrative center in the West Bank.
-
B.
Said Malek
Said Malek is the father of Academy Award–winning actor Rami Malek.
-
C.
Abir Muhaisen
Abir Muhaisen is one of the adopted daughters of Queen Alia al-Hussein of Jordan, raised within the Jordanian royal family.
-
D.
Omar Samhan
Omar Samhan is an American former professional basketball center best known for starring at Saint Mary's College, where he became one of the program's most dominant and recognizable players.
-
E.
Hassan Aref
Hassan Aref was a prominent physicist and engineer known for his pioneering contributions to fluid dynamics, particularly in vortex dynamics and chaotic advection.
- 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: Henry Barakat Triple: [Faten Hamama, workedWith, Henry Barakat]
Generated description
Henry Barakat was a prominent Egyptian film director and one of the leading figures of classical Egyptian cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Henry Barakat Target entity description: Henry Barakat was a prominent Egyptian film director and one of the leading figures of classical Egyptian cinema.
-
A.
Issa Kassis
Issa Kassis is a Palestinian politician who serves as the mayor of Ramallah, a major cultural and administrative center in the West Bank.
-
B.
Said Malek
Said Malek is the father of Academy Award–winning actor Rami Malek.
-
C.
Abir Muhaisen
Abir Muhaisen is one of the adopted daughters of Queen Alia al-Hussein of Jordan, raised within the Jordanian royal family.
-
D.
Omar Samhan
Omar Samhan is an American former professional basketball center best known for starring at Saint Mary's College, where he became one of the program's most dominant and recognizable players.
-
E.
Hassan Aref
Hassan Aref was a prominent physicist and engineer known for his pioneering contributions to fluid dynamics, particularly in vortex dynamics and chaotic advection.
- 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_69a494060e148190abb42f971242c197 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bcad47a08190895769611798f67f |
completed | March 1, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac7642ff0c81909b323ac328b18e2e |
completed | March 7, 2026, 7:02 p.m. |
| NEDg | Description generation | batch_69ac76f20f308190be3c831eb2d59763 |
completed | March 7, 2026, 7:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac77585c708190b5f4b239d9574cd7 |
completed | March 7, 2026, 7:07 p.m. |
Created at: March 1, 2026, 7:45 p.m.