Triple
T12277851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madame Medusa |
E292636
|
entity |
| Predicate | enemy |
P4567
|
FINISHED |
| Object |
Penny
Penny is the brave young orphan girl from Disney’s animated film “The Rescuers,” known for her courage in standing up to the villainous Madame Medusa.
|
E292635
|
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: Penny | Statement: [Madame Medusa, enemy, Penny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Penny Context triple: [Madame Medusa, enemy, Penny]
-
A.
Penny
Penny Pritzker is an American billionaire businesswoman, civic leader, and former U.S. Secretary of Commerce in the Obama administration.
-
B.
Penny
Penny is a familiar diminutive form of the given name Penelope, often used as a friendly and informal nickname.
-
C.
Penny
Penny is a central character in the educational context of "Teachers," likely portrayed as a key figure around whom classroom stories and interactions revolve.
-
D.
Penny
Penny is a friendly, aspiring actress and waitress who becomes the sociable, down-to-earth neighbor and later close friend and love interest of the main nerdy characters in the sitcom "The Big Bang Theory."
-
E.
Penny
Penny is a character in Jim Jarmusch’s film "Broken Flowers," one of the former lovers visited by the protagonist during his cross-country journey.
- 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: Penny Triple: [Madame Medusa, enemy, Penny]
Generated description
Penny is the brave young orphan girl from Disney’s animated film “The Rescuers,” known for her courage in standing up to the villainous Madame Medusa.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Penny Target entity description: Penny is the brave young orphan girl from Disney’s animated film “The Rescuers,” known for her courage in standing up to the villainous Madame Medusa.
-
A.
Penny
chosen
Penny is the brave young orphan girl who is rescued by mice in Disney's animated adventure film "The Rescuers."
-
B.
Penny
Penny is the troubled and delusional mother of Arthur Fleck in the 2019 film "Joker."
-
C.
Penny
Penny is a friendly, aspiring actress and waitress who becomes the sociable, down-to-earth neighbor and later close friend and love interest of the main nerdy characters in the sitcom "The Big Bang Theory."
-
D.
Penny
Penny is a character in Jim Jarmusch’s film "Broken Flowers," one of the former lovers visited by the protagonist during his cross-country journey.
-
E.
Penny
Penny is the given name of American actress Penny Johnson Jerald, known for her roles in series such as "24" and "Star Trek: Deep Space Nine."
- F. None of above.
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_69d6ab6856488190b5d31178d5015f8e |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cf06cf08190ac8671dd9bbed03d |
completed | April 10, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e6d72d081908c8697257df712f1 |
completed | May 2, 2026, 3:55 p.m. |
| NEDg | Description generation | batch_69f620759f348190baa9af5b33d4e37f |
completed | May 2, 2026, 4:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f624bf23948190b182e4c31564d210 |
completed | May 2, 2026, 4:22 p.m. |
Created at: April 8, 2026, 9:52 p.m.