Triple
T20329608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sebastian Cabot |
E492438
|
entity |
| Predicate | televisionRole |
P1668
|
FINISHED |
| Object |
Mr. French
Mr. French is the proper, British valet character best known from the 1960s American sitcom "Family Affair."
|
E1424593
|
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: Mr. French | Statement: [Sebastian Cabot, televisionRole, Mr. French]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mr. French Context triple: [Sebastian Cabot, televisionRole, Mr. French]
-
A.
William Barfée
William Barfée is a socially awkward, allergy-prone spelling prodigy known for his “magic foot” spelling technique in the musical comedy *The 25th Annual Putnam County Spelling Bee*.
-
B.
Mr. Maellard
Mr. Maellard is a wealthy, stern yet caring businessman and the adoptive father of Pops in the animated series "Regular Show."
-
C.
Mr. Franks
Mr. Franks is a music producer best known for his work with the hip-hop collective Legend.
-
D.
Monsieur
Monsieur was the traditional honorific title used at the French court for Philippe I, Duke of Orléans, the younger brother of King Louis XIV.
-
E.
Maurice Bendrix
Maurice Bendrix is the jealous and tormented writer whose obsessive love affair drives the emotional and moral conflict in Graham Greene’s novel and its 1999 film adaptation, "The End of the Affair."
- 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: Mr. French Triple: [Sebastian Cabot, televisionRole, Mr. French]
Generated description
Mr. French is the proper, British valet character best known from the 1960s American sitcom "Family Affair."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mr. French Target entity description: Mr. French is the proper, British valet character best known from the 1960s American sitcom "Family Affair."
-
A.
William Barfée
William Barfée is a socially awkward, allergy-prone spelling prodigy known for his “magic foot” spelling technique in the musical comedy *The 25th Annual Putnam County Spelling Bee*.
-
B.
Mr. Maellard
Mr. Maellard is a wealthy, stern yet caring businessman and the adoptive father of Pops in the animated series "Regular Show."
-
C.
Mr. Franks
Mr. Franks is a music producer best known for his work with the hip-hop collective Legend.
-
D.
Monsieur
Monsieur was the traditional honorific title used at the French court for Philippe I, Duke of Orléans, the younger brother of King Louis XIV.
-
E.
Maurice Bendrix
Maurice Bendrix is the jealous and tormented writer whose obsessive love affair drives the emotional and moral conflict in Graham Greene’s novel and its 1999 film adaptation, "The End of the Affair."
- 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_69e0b4a1a09881908d97270d6971a25a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e677e6f5d08190bedf376bbb999ebc |
completed | April 20, 2026, 7 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a086951af848190ae8085b7917ea551 |
completed | May 16, 2026, 12:55 p.m. |
| NEDg | Description generation | batch_6a0869e2a80481909105005195330dc9 |
completed | May 16, 2026, 12:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a086a6ad6c88190be1770758d2e1bbf |
completed | May 16, 2026, 1 p.m. |
Created at: April 16, 2026, 11:22 a.m.