Triple
T20570329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Police Academy 5: Assignment Miami Beach |
E505078
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Zed
Zed is a loud, eccentric former gang leader turned bumbling police officer in the Police Academy comedy film series.
|
E1438018
|
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: Zed | Statement: [Police Academy 5: Assignment Miami Beach, mainCharacter, Zed]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zed Context triple: [Police Academy 5: Assignment Miami Beach, mainCharacter, Zed]
-
A.
Zed
Zed is the rebellious Exterminator protagonist played by Sean Connery in the 1974 science fiction film "Zardoz."
-
B.
Zed
Zed is the central protagonist of the crime film "Killing Zoe," a professional safecracker drawn into a violent Parisian bank heist.
-
C.
Zed
Zed is the stern, no-nonsense head of the secret government agency overseeing extraterrestrial activity in the "Men in Black" film series.
-
D.
Zan
Zan is a collective term for a group of closely related Kartvelian languages spoken primarily in western Georgia, including Mingrelian and Laz.
-
E.
Zedus
Zedus is a giant, reptilian kaiju antagonist from the film "Gamera the Brave," known for battling the young Gamera.
- 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: Zed Triple: [Police Academy 5: Assignment Miami Beach, mainCharacter, Zed]
Generated description
Zed is a loud, eccentric former gang leader turned bumbling police officer in the Police Academy comedy film series.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zed Target entity description: Zed is a loud, eccentric former gang leader turned bumbling police officer in the Police Academy comedy film series.
-
A.
Zed
Zed is the stern, no-nonsense head of the secret government agency overseeing extraterrestrial activity in the "Men in Black" film series.
-
B.
Zed
Zed is the rebellious Exterminator protagonist played by Sean Connery in the 1974 science fiction film "Zardoz."
-
C.
Zed
Zed is the central protagonist of the crime film "Killing Zoe," a professional safecracker drawn into a violent Parisian bank heist.
-
D.
Zan
Zan is a collective term for a group of closely related Kartvelian languages spoken primarily in western Georgia, including Mingrelian and Laz.
-
E.
Zedus
Zedus is a giant, reptilian kaiju antagonist from the film "Gamera the Brave," known for battling the young Gamera.
- 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_69e0b4b721588190993ac7b0a9be2736 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a7a5b0688190b45d0fa993c4765c |
completed | April 20, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08ace1b0288190a7e68ad0e83e11b6 |
completed | May 16, 2026, 5:44 p.m. |
| NEDg | Description generation | batch_6a08ad7f0cac81908bd2aa0cafe95b15 |
completed | May 16, 2026, 5:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08adeae6d881909b19929e0e5aa1c9 |
completed | May 16, 2026, 5:48 p.m. |
Created at: April 16, 2026, 11:39 a.m.