Triple
T15479615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kassis |
E376879
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
André Kassis
André Kassis is a person notable enough to be recognized as a prominent bearer of the surname Kassis.
|
E1160713
|
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: André Kassis | Statement: [Kassis, hasNotableBearer, André Kassis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: André Kassis Context triple: [Kassis, hasNotableBearer, André Kassis]
-
A.
Adnan Kassar
Adnan Kassar is a prominent Lebanese businessman and banker known for his leadership in international commerce and significant contributions to economic development and education in Lebanon.
-
B.
Fawaz Akhras
Fawaz Akhras is a London-based Syrian cardiologist best known as the father of Syria’s First Lady, Asma al-Assad.
-
C.
Antoine Nahas
Antoine Nahas was a Lebanese architect best known for designing the National Museum of Beirut, a landmark institution of Lebanon’s cultural heritage.
-
D.
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.
-
E.
Arthur Sadoun
Arthur Sadoun is a French advertising executive and the chief executive of Publicis Groupe, one of the world’s largest communications and marketing services companies.
- 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: André Kassis Triple: [Kassis, hasNotableBearer, André Kassis]
Generated description
André Kassis is a person notable enough to be recognized as a prominent bearer of the surname Kassis.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: André Kassis Target entity description: André Kassis is a person notable enough to be recognized as a prominent bearer of the surname Kassis.
-
A.
Adnan Kassar
Adnan Kassar is a prominent Lebanese businessman and banker known for his leadership in international commerce and significant contributions to economic development and education in Lebanon.
-
B.
Fawaz Akhras
Fawaz Akhras is a London-based Syrian cardiologist best known as the father of Syria’s First Lady, Asma al-Assad.
-
C.
Antoine Nahas
Antoine Nahas was a Lebanese architect best known for designing the National Museum of Beirut, a landmark institution of Lebanon’s cultural heritage.
-
D.
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.
-
E.
Arthur Sadoun
Arthur Sadoun is a French advertising executive and the chief executive of Publicis Groupe, one of the world’s largest communications and marketing services companies.
- 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f8a77a081909f12f13660452f4a |
completed | April 16, 2026, 1:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff36595bfc8190a0d60b3cb875ccc5 |
completed | May 9, 2026, 1:27 p.m. |
| NEDg | Description generation | batch_69ff376dac388190ab3b7e3553d2de29 |
completed | May 9, 2026, 1:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff382f1bbc8190810d0d825430f9ea |
completed | May 9, 2026, 1:35 p.m. |
Created at: April 10, 2026, 3:34 a.m.