Triple
T21634169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vivarium |
E533910
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Pingpong Film
Pingpong Film is a film production company known for producing the surreal sci-fi thriller "Vivarium."
|
E1493955
|
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: Pingpong Film | Statement: [Vivarium, productionCompany, Pingpong Film]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pingpong Film Context triple: [Vivarium, productionCompany, Pingpong Film]
-
A.
Le Ping-Pong
Le Ping-Pong is an absurdist play by French dramatist Arthur Adamov that explores themes of alienation and mechanization through the obsessive world of a pinball arcade.
-
B.
Ping Pong Productions
Ping Pong Productions is a television production company best known for creating and producing the popular TLC medical reality series "Dr. Pimple Popper."
-
C.
Pie Films
Pie Films is a film production company known for producing the psychological drama "The Lost Daughter."
-
D.
Pang and Pong
Pang and Pong are a performing duo known for their on-stage collaborations with the artist Ping.
-
E.
Piki Films
Piki Films is a New Zealand-based film and television production company known for backing distinctive, often offbeat projects such as Taika Waititi’s Oscar-winning film "Jojo Rabbit."
- 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: Pingpong Film Triple: [Vivarium, productionCompany, Pingpong Film]
Generated description
Pingpong Film is a film production company known for producing the surreal sci-fi thriller "Vivarium."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pingpong Film Target entity description: Pingpong Film is a film production company known for producing the surreal sci-fi thriller "Vivarium."
-
A.
Le Ping-Pong
Le Ping-Pong is an absurdist play by French dramatist Arthur Adamov that explores themes of alienation and mechanization through the obsessive world of a pinball arcade.
-
B.
Ping Pong Productions
Ping Pong Productions is a television production company best known for creating and producing the popular TLC medical reality series "Dr. Pimple Popper."
-
C.
Pie Films
Pie Films is a film production company known for producing the psychological drama "The Lost Daughter."
-
D.
Pang and Pong
Pang and Pong are a performing duo known for their on-stage collaborations with the artist Ping.
-
E.
Piki Films
Piki Films is a New Zealand-based film and television production company known for backing distinctive, often offbeat projects such as Taika Waititi’s Oscar-winning film "Jojo Rabbit."
- 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_69e0c465ae7481908577b7209fdb2a77 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef52192e388190a3f316e33f452561 |
completed | April 27, 2026, 12:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a0f9bc2f481908ed5ba8292adf47f |
completed | May 17, 2026, 6:57 p.m. |
| NEDg | Description generation | batch_6a0a105ec6a08190a7b6d89068e4e369 |
completed | May 17, 2026, 7 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a113606f8819093b398915a291fa1 |
completed | May 17, 2026, 7:04 p.m. |
Created at: April 16, 2026, 6:35 p.m.