Triple
T14864250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krisztinaváros |
E349576
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object |
Tabán Cinema
Tabán Cinema is a historic movie theater located in the Krisztinaváros district of Budapest, known for screening art films and hosting cultural events.
|
E1123472
|
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: Tabán Cinema | Statement: [Krisztinaváros, hasLandmark, Tabán Cinema]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tabán Cinema Context triple: [Krisztinaváros, hasLandmark, Tabán Cinema]
-
A.
Yara Cinema
Yara Cinema is a prominent and historic movie theater in Havana, Cuba, known as a cultural landmark and popular gathering place in the Vedado district.
-
B.
Tobogán Films
Tobogán Films is a film production company known for producing the Mexican romantic comedy "Sólo con tu pareja."
-
C.
Arsenal Kino
Arsenal Kino is a renowned Berlin art-house cinema and film archive known for its curated programs of experimental, independent, and international films.
-
D.
Filmhaus
Filmhaus is a film production company known for producing the psychological thriller "House of Games."
-
E.
Cinema de Lux
Cinema de Lux is a premium movie theater complex known for offering an upscale cinema experience with enhanced amenities and comfort.
- 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: Tabán Cinema Triple: [Krisztinaváros, hasLandmark, Tabán Cinema]
Generated description
Tabán Cinema is a historic movie theater located in the Krisztinaváros district of Budapest, known for screening art films and hosting cultural events.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tabán Cinema Target entity description: Tabán Cinema is a historic movie theater located in the Krisztinaváros district of Budapest, known for screening art films and hosting cultural events.
-
A.
Yara Cinema
Yara Cinema is a prominent and historic movie theater in Havana, Cuba, known as a cultural landmark and popular gathering place in the Vedado district.
-
B.
Tobogán Films
Tobogán Films is a film production company known for producing the Mexican romantic comedy "Sólo con tu pareja."
-
C.
Arsenal Kino
Arsenal Kino is a renowned Berlin art-house cinema and film archive known for its curated programs of experimental, independent, and international films.
-
D.
Filmhaus
Filmhaus is a film production company known for producing the psychological thriller "House of Games."
-
E.
Cinema de Lux
Cinema de Lux is a premium movie theater complex known for offering an upscale cinema experience with enhanced amenities and comfort.
- 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_69d822ed7e1881909b90fca143ad7e34 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded574d0ec8190a6afed672ba6c2f9 |
completed | April 15, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe650e8aec8190acd4a9cb9cad2039 |
completed | May 8, 2026, 10:34 p.m. |
| NEDg | Description generation | batch_69fe65ac6a5c81908621dc17edc6b04f |
completed | May 8, 2026, 10:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe6697fe3881908aae42abe56d86f8 |
completed | May 8, 2026, 10:41 p.m. |
Created at: April 10, 2026, 1:54 a.m.