Triple
T2015600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frederick Tudor |
E43787
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Ice King
Ice King is the nickname of Frederick Tudor, a 19th-century American entrepreneur who pioneered the international ice trade by shipping harvested ice worldwide.
|
E225805
|
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: Ice King | Statement: [Frederick Tudor, nickname, Ice King]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ice King Context triple: [Frederick Tudor, nickname, Ice King]
-
A.
Olaf
Olaf is a masculine given name of Old Norse origin, commonly used in Germanic and Scandinavian countries.
-
B.
Kristoff
Kristoff is a rugged, kind-hearted ice harvester and one of the central human protagonists in Disney's animated film "Frozen."
-
C.
Jack Frost
Jack Frost is a pseudonym used by Bob Dylan for his role as a record producer on several of his later albums.
-
D.
Elsa
Elsa is a feminine given name of Germanic origin, widely recognized today through its use for the main character in Disney's animated film "Frozen."
-
E.
Prince Charming
Prince Charming is the idealized fairytale prince known for rescuing and marrying Cinderella in the classic Disney story.
- 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: Ice King Triple: [Frederick Tudor, nickname, Ice King]
Generated description
Ice King is the nickname of Frederick Tudor, a 19th-century American entrepreneur who pioneered the international ice trade by shipping harvested ice worldwide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ice King Target entity description: Ice King is the nickname of Frederick Tudor, a 19th-century American entrepreneur who pioneered the international ice trade by shipping harvested ice worldwide.
-
A.
Olaf
Olaf is a masculine given name of Old Norse origin, commonly used in Germanic and Scandinavian countries.
-
B.
Kristoff
Kristoff is a rugged, kind-hearted ice harvester and one of the central human protagonists in Disney's animated film "Frozen."
-
C.
Jack Frost
Jack Frost is a pseudonym used by Bob Dylan for his role as a record producer on several of his later albums.
-
D.
Elsa
Elsa is a feminine given name of Germanic origin, widely recognized today through its use for the main character in Disney's animated film "Frozen."
-
E.
Prince Charming
Prince Charming is the idealized fairytale prince known for rescuing and marrying Cinderella in the classic Disney story.
- 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_69a88716e9f08190946313fdc949e3cf |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb8cb16048190bc626685fbb5f707 |
completed | March 7, 2026, 5:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae0aef0fe88190adf9cd218cf7d8b4 |
completed | March 8, 2026, 11:49 p.m. |
| NEDg | Description generation | batch_69ae0c1ea0388190b44af2223517129e |
completed | March 8, 2026, 11:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae0c7719e881909059cef2c513a05a |
completed | March 8, 2026, 11:55 p.m. |
Created at: March 4, 2026, 7:37 p.m.