Triple
T5227796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Robe |
E118034
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object |
Michael Ansara
Michael Ansara was a Syrian-American character actor best known for his deep voice and frequent roles in Westerns and science fiction, including memorable appearances in series like Star Trek and Babylon 5.
|
E502679
|
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: Michael Ansara | Statement: [The Robe, portrayedBy, Michael Ansara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Ansara Context triple: [The Robe, portrayedBy, Michael Ansara]
-
A.
Sam Joshi
Sam Joshi is an American politician who serves as the mayor of Edison Township, New Jersey.
-
B.
Abdul Mateen
Abdul Mateen is a Bruneian prince and public figure known for his military career, international diplomacy, and prominent presence in regional and global events.
-
C.
Suresh Ayyar
Suresh Ayyar is an editor known for his work on the acclaimed Australian memoir "Romulus, My Father."
-
D.
Dileep Rao
Dileep Rao is an American actor known for his supporting roles in major films such as Avatar, Drag Me to Hell, and Inception.
-
E.
Arif Masood
Arif Masood is a Pakistani architect best known for designing the iconic Pakistan Monument in Islamabad.
- 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: Michael Ansara Triple: [The Robe, portrayedBy, Michael Ansara]
Generated description
Michael Ansara was a Syrian-American character actor best known for his deep voice and frequent roles in Westerns and science fiction, including memorable appearances in series like Star Trek and Babylon 5.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Ansara Target entity description: Michael Ansara was a Syrian-American character actor best known for his deep voice and frequent roles in Westerns and science fiction, including memorable appearances in series like Star Trek and Babylon 5.
-
A.
Sam Joshi
Sam Joshi is an American politician who serves as the mayor of Edison Township, New Jersey.
-
B.
Abdul Mateen
Abdul Mateen is a Bruneian prince and public figure known for his military career, international diplomacy, and prominent presence in regional and global events.
-
C.
Suresh Ayyar
Suresh Ayyar is an editor known for his work on the acclaimed Australian memoir "Romulus, My Father."
-
D.
Dileep Rao
Dileep Rao is an American actor known for his supporting roles in major films such as Avatar, Drag Me to Hell, and Inception.
-
E.
Arif Masood
Arif Masood is a Pakistani architect best known for designing the iconic Pakistan Monument in Islamabad.
- 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_69bd4466fb8c819083b806a79414d7e4 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7addbdb88190baf9f47fc4cbb7fc |
completed | March 20, 2026, 4:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beeffea8f481909c86c932781c4e2a |
completed | March 21, 2026, 7:22 p.m. |
| NEDg | Description generation | batch_69bef097f5e48190b8a28995f345c764 |
completed | March 21, 2026, 7:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bef0f0ceb081908600d75b6e52f45a |
completed | March 21, 2026, 7:26 p.m. |
Created at: March 20, 2026, 1:48 p.m.