Triple
T9997851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karel Roden |
E197247
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
The Zookeeper
The Zookeeper is a film featuring Czech actor Karel Roden in a prominent role.
|
E833718
|
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: The Zookeeper | Statement: [Karel Roden, notableWork, The Zookeeper]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Zookeeper Context triple: [Karel Roden, notableWork, The Zookeeper]
-
A.
The Zoo
"The Zoo" is the raucous student section and fan base known for creating an intense home-field atmosphere at Arizona Stadium, home of the University of Arizona Wildcats football team.
-
B.
The Zoo
"The Zoo" is a popular nickname for Kalamazoo, Michigan, reflecting the city's lively character and strong local identity.
-
C.
The Zoo
"The Zoo" is a popular hard rock song by the German band Scorpions, known for its heavy riff and frequent appearance in their live performances.
-
D.
Zoo City
Zoo City is a noir-infused urban fantasy novel by Lauren Beukes, set in an alternate Johannesburg where people magically manifest animal companions as a mark of their criminal guilt.
-
E.
El gran zoo
El gran zoo is a celebrated poetry collection by Cuban writer Nicolás Guillén that uses the metaphor of a fantastical zoo to deliver sharp social and political satire.
- 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: The Zookeeper Triple: [Karel Roden, notableWork, The Zookeeper]
Generated description
The Zookeeper is a film featuring Czech actor Karel Roden in a prominent role.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: The Zookeeper Target entity description: The Zookeeper is a film featuring Czech actor Karel Roden in a prominent role.
-
A.
The Zoo
"The Zoo" is the raucous student section and fan base known for creating an intense home-field atmosphere at Arizona Stadium, home of the University of Arizona Wildcats football team.
-
B.
The Zoo
"The Zoo" is a popular nickname for Kalamazoo, Michigan, reflecting the city's lively character and strong local identity.
-
C.
The Zoo
"The Zoo" is a popular hard rock song by the German band Scorpions, known for its heavy riff and frequent appearance in their live performances.
-
D.
Zoo City
Zoo City is a noir-infused urban fantasy novel by Lauren Beukes, set in an alternate Johannesburg where people magically manifest animal companions as a mark of their criminal guilt.
-
E.
El gran zoo
El gran zoo is a celebrated poetry collection by Cuban writer Nicolás Guillén that uses the metaphor of a fantastical zoo to deliver sharp social and political satire.
- 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_69ca82f3b61c81908ecc2c1c96dbc2e4 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdcc8aa1a881909879a694496f11a5 |
completed | April 2, 2026, 1:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d258439fe88190b17da69f542ecf61 |
completed | April 5, 2026, 12:40 p.m. |
| NEDg | Description generation | batch_69d259701e488190b288c9f523a1ec87 |
completed | April 5, 2026, 12:45 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d259da25e081909ac184f4fa80c57e |
completed | April 5, 2026, 12:47 p.m. |
Created at: March 30, 2026, 8:51 p.m.