Triple
T20161971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eileen |
E491729
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Sir Reginald
Sir Reginald is a fictional character, likely a formal or aristocratic figure, appearing in a story or work centered around someone named Eileen.
|
E1415050
|
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: Sir Reginald | Statement: [Eileen, hasCharacter, Sir Reginald]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sir Reginald Context triple: [Eileen, hasCharacter, Sir Reginald]
-
A.
Sir Percival
Sir Percival is a legendary knight of King Arthur’s Round Table, best known for his role in the quest for the Holy Grail in Arthurian romance.
-
B.
Sir Nicholas
Sir Nicholas is an alternate name for Nick, typically used as a more formal or honorific version of the given name.
-
C.
Sir Te
Sir Te is a respected nobleman and mentor figure in the film "Crouching Tiger, Hidden Dragon," known for safeguarding the legendary sword Green Destiny.
-
D.
Sir Robin Janvrin
Sir Robin Janvrin is a British former diplomat and courtier best known for serving as Private Secretary to Queen Elizabeth II.
-
E.
Lord Alverstone
Lord Alverstone was an English lawyer, Conservative politician, and Lord Chief Justice of England who played a prominent role in several important early 20th-century legal cases.
- 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: Sir Reginald Triple: [Eileen, hasCharacter, Sir Reginald]
Generated description
Sir Reginald is a fictional character, likely a formal or aristocratic figure, appearing in a story or work centered around someone named Eileen.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sir Reginald Target entity description: Sir Reginald is a fictional character, likely a formal or aristocratic figure, appearing in a story or work centered around someone named Eileen.
-
A.
Sir Percival
Sir Percival is a legendary knight of King Arthur’s Round Table, best known for his role in the quest for the Holy Grail in Arthurian romance.
-
B.
Sir Nicholas
Sir Nicholas is an alternate name for Nick, typically used as a more formal or honorific version of the given name.
-
C.
Sir Te
Sir Te is a respected nobleman and mentor figure in the film "Crouching Tiger, Hidden Dragon," known for safeguarding the legendary sword Green Destiny.
-
D.
Sir Robin Janvrin
Sir Robin Janvrin is a British former diplomat and courtier best known for serving as Private Secretary to Queen Elizabeth II.
-
E.
Lord Alverstone
Lord Alverstone was an English lawyer, Conservative politician, and Lord Chief Justice of England who played a prominent role in several important early 20th-century legal cases.
- 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e667e505888190a05e26a3c5a0ede1 |
completed | April 20, 2026, 5:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08347b41d8819086bd36e5e04c69ad |
completed | May 16, 2026, 9:10 a.m. |
| NEDg | Description generation | batch_6a08359866c481908aa30ac61e18bcb0 |
completed | May 16, 2026, 9:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08361f64a08190af3305685a50f001 |
completed | May 16, 2026, 9:17 a.m. |
Created at: April 11, 2026, 11:34 p.m.