Triple
T20488676
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Ansara |
E502679
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Ansara
Ansara is a surname most notably associated with Michael Ansara, a Syrian-American actor known for his roles in film and television.
|
E1434004
|
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: Ansara | Statement: [Michael Ansara, familyName, Ansara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ansara Context triple: [Michael Ansara, familyName, Ansara]
-
A.
Andarab
Andarab is a town in northeastern Afghanistan known for its strategic valley location and historical significance within the Hindu Kush region.
-
B.
Shuafat
Shuafat is a Palestinian neighborhood and refugee camp in East Jerusalem known for its dense population, complex political status, and challenging living conditions.
-
C.
Rushan
Rushan is a county-level coastal city in eastern Shandong Province, China, known for its fishing industry, beaches, and marine-based economy.
-
D.
Samlah
Samlah is a minor biblical figure listed in Genesis 36 as one of the early kings of Edom.
-
E.
Ceyrat
Ceyrat is a commune in the Puy-de-Dôme department of central France, located in the Auvergne region near the city of Clermont-Ferrand.
- 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: Ansara Triple: [Michael Ansara, familyName, Ansara]
Generated description
Ansara is a surname most notably associated with Michael Ansara, a Syrian-American actor known for his roles in film and television.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ansara Target entity description: Ansara is a surname most notably associated with Michael Ansara, a Syrian-American actor known for his roles in film and television.
-
A.
Andarab
Andarab is a town in northeastern Afghanistan known for its strategic valley location and historical significance within the Hindu Kush region.
-
B.
Shuafat
Shuafat is a Palestinian neighborhood and refugee camp in East Jerusalem known for its dense population, complex political status, and challenging living conditions.
-
C.
Rushan
Rushan is a county-level coastal city in eastern Shandong Province, China, known for its fishing industry, beaches, and marine-based economy.
-
D.
Samlah
Samlah is a minor biblical figure listed in Genesis 36 as one of the early kings of Edom.
-
E.
Ceyrat
Ceyrat is a commune in the Puy-de-Dôme department of central France, located in the Auvergne region near the city of Clermont-Ferrand.
- 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_69e0b4b0373881909dd3e9387f82eab4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69b5c6f84819087d813be3542ed33 |
completed | April 20, 2026, 9:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0893b3c77c81908aedd753ec251577 |
completed | May 16, 2026, 3:56 p.m. |
| NEDg | Description generation | batch_6a08953618088190a9336d0ab6445c9d |
completed | May 16, 2026, 4:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0895c4a0a48190ba43d065c9766fd8 |
completed | May 16, 2026, 4:05 p.m. |
Created at: April 16, 2026, 11:34 a.m.