Triple
T14957715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Winnifred the Woebegone |
E372974
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
Fred
Fred is the irreverent, unconventional princess from the musical "Once Upon a Mattress," formally known as Princess Winnifred the Woebegone.
|
E1129281
|
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: Fred | Statement: [Princess Winnifred the Woebegone, alsoKnownAs, Fred]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fred Context triple: [Princess Winnifred the Woebegone, alsoKnownAs, Fred]
-
A.
Fred
Fred is the son of Fred Trump Jr. and the nephew of former U.S. President Donald Trump.
-
B.
Fred
Fred is a French luxury jewelry brand renowned for its elegant, contemporary designs and high-end craftsmanship, owned by the LVMH group.
-
C.
Fred
Fred is a prolific Brazilian striker best known for his goal-scoring exploits with Fluminense and the Brazilian national team.
-
D.
Fred
Fred is the given name of Fred J. Koenekamp, an American cinematographer known for his work on films such as "Patton" and "The Towering Inferno."
-
E.
Fred
Fred is the given name of Fredro Starr, an American rapper and actor best known as a member of the hip hop group Onyx and for his roles in film and television.
- 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: Fred Triple: [Princess Winnifred the Woebegone, alsoKnownAs, Fred]
Generated description
Fred is the irreverent, unconventional princess from the musical "Once Upon a Mattress," formally known as Princess Winnifred the Woebegone.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fred Target entity description: Fred is the irreverent, unconventional princess from the musical "Once Upon a Mattress," formally known as Princess Winnifred the Woebegone.
-
A.
Fred
Fred is a character in Noël Coward’s comedy play "Present Laughter," typically portrayed as a close friend and associate of the egocentric actor Garry Essendine.
-
B.
Fred
Fred is the prehistoric, loudmouthed but lovable main character of the animated television series "The Flintstones."
-
C.
Fred
Fred is a laid-back, comic book–obsessed college student and enthusiastic member of the superhero team in Disney's animated film "Big Hero 6."
-
D.
Fred
Fred is the given name of Fred Rogers, the beloved American television host and creator of the children's program "Mister Rogers' Neighborhood."
-
E.
Fred
Fred is a Swedish surname most notably borne by actress Gunnel Fred.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6cc73848190ac181782b20dc838 |
completed | April 15, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7e9e74fc8190bdd10a25c39829f3 |
completed | May 9, 2026, 12:23 a.m. |
| NEDg | Description generation | batch_69fe83269020819085b904e080578580 |
completed | May 9, 2026, 12:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe83ccc73881909c28c53052c4cd86 |
completed | May 9, 2026, 12:46 a.m. |
Created at: April 10, 2026, 2:40 a.m.