Triple
T10200313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belqasim Haftar |
E238865
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Belqasim
Belqasim is a personal given name of Arabic origin, used primarily in North African and Middle Eastern cultures.
|
E847323
|
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: Belqasim | Statement: [Belqasim Haftar, givenName, Belqasim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belqasim Context triple: [Belqasim Haftar, givenName, Belqasim]
-
A.
Al-Hareeq
Al-Hareeq is a town in central Saudi Arabia known for its agricultural activity, particularly date palm cultivation, within the Riyadh region.
-
B.
Abu Lulu
Abu Lulu was a Persian slave and craftsman historically known for assassinating the second Rashidun caliph, Umar ibn al-Khattab, in Medina.
-
C.
Sahnun
Sahnun was a prominent 9th-century Islamic jurist from North Africa whose compilation of legal opinions, the Mudawwana, became a foundational text of the Maliki school of Sunni jurisprudence.
-
D.
Khaldoon
Khaldoon is an Arabic masculine given name commonly used in the Middle East.
-
E.
Mohandessin
Mohandessin is a prominent, upscale district in Giza, Egypt, known for its residential neighborhoods, commercial avenues, and vibrant urban life.
- 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: Belqasim Triple: [Belqasim Haftar, givenName, Belqasim]
Generated description
Belqasim is a personal given name of Arabic origin, used primarily in North African and Middle Eastern cultures.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Belqasim Target entity description: Belqasim is a personal given name of Arabic origin, used primarily in North African and Middle Eastern cultures.
-
A.
Al-Hareeq
Al-Hareeq is a town in central Saudi Arabia known for its agricultural activity, particularly date palm cultivation, within the Riyadh region.
-
B.
Abu Lulu
Abu Lulu was a Persian slave and craftsman historically known for assassinating the second Rashidun caliph, Umar ibn al-Khattab, in Medina.
-
C.
Sahnun
Sahnun was a prominent 9th-century Islamic jurist from North Africa whose compilation of legal opinions, the Mudawwana, became a foundational text of the Maliki school of Sunni jurisprudence.
-
D.
Khaldoon
Khaldoon is an Arabic masculine given name commonly used in the Middle East.
-
E.
Mohandessin
Mohandessin is a prominent, upscale district in Giza, Egypt, known for its residential neighborhoods, commercial avenues, and vibrant urban life.
- 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_69ca84e1ea088190b38162e43d4cfa8f |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdee3f3bac8190a63a81edffe7cda7 |
completed | April 2, 2026, 4:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d317f40f0c8190a3d966c934cc20f7 |
completed | April 6, 2026, 2:18 a.m. |
| NEDg | Description generation | batch_69d3188886908190ba0a5539ce942980 |
completed | April 6, 2026, 2:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d31c4fb8288190bbc6b3d4a79dafb1 |
completed | April 6, 2026, 2:37 a.m. |
Created at: March 30, 2026, 9:14 p.m.