Triple
T504909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cinderella (1950 film) |
E10482
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Prince Charming
Prince Charming is the idealized fairytale prince known for rescuing and marrying Cinderella in the classic Disney story.
|
E62934
|
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: Prince Charming | Statement: [Cinderella (1950 film), mainCharacter, Prince Charming]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Prince Charming Context triple: [Cinderella (1950 film), mainCharacter, Prince Charming]
-
A.
Nicholas
Nicholas is a masculine given name of Greek origin, commonly used in many cultures and historically borne by numerous saints, rulers, and notable figures.
-
B.
Robert
Robert is a common masculine given name of Germanic origin, widely used in English-speaking countries.
-
C.
Roy
Roy is a common masculine given name of Celtic origin, often used independently or as a nickname for longer names.
-
D.
Alfred
Alfred is a masculine given name of English origin, historically popular in Anglo-Saxon and later English-speaking cultures.
-
E.
Olaf
Olaf is a masculine given name of Old Norse origin, commonly used in Germanic and Scandinavian countries.
- 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: Prince Charming Triple: [Cinderella (1950 film), mainCharacter, Prince Charming]
Generated description
Prince Charming is the idealized fairytale prince known for rescuing and marrying Cinderella in the classic Disney story.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Prince Charming Target entity description: Prince Charming is the idealized fairytale prince known for rescuing and marrying Cinderella in the classic Disney story.
-
A.
Nicholas
Nicholas is a masculine given name of Greek origin, commonly used in many cultures and historically borne by numerous saints, rulers, and notable figures.
-
B.
Robert
Robert is a common masculine given name of Germanic origin, widely used in English-speaking countries.
-
C.
Roy
Roy is a common masculine given name of Celtic origin, often used independently or as a nickname for longer names.
-
D.
Alfred
Alfred is a masculine given name of English origin, historically popular in Anglo-Saxon and later English-speaking cultures.
-
E.
Olaf
Olaf is a masculine given name of Old Norse origin, commonly used in Germanic and Scandinavian countries.
- 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f149bd1c81908ff58ac504ace2bf |
completed | Feb. 28, 2026, 1:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a48a7b71848190ab3e69fc84779301 |
completed | March 1, 2026, 6:50 p.m. |
| NEDg | Description generation | batch_69a48c1329f0819093fa4eb579075243 |
completed | March 1, 2026, 6:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a48c688fcc81909d79068875b89aee |
completed | March 1, 2026, 6:58 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.