Triple
T13036080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 42 (school) |
E326563
|
entity |
| Predicate | hasCampus |
P116
|
FINISHED |
| Object |
42 Rio
42 Rio is a tuition-free, peer-to-peer programming school in Rio de Janeiro that follows the innovative, project-based learning model of the international 42 network.
|
E1017937
|
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: 42 Rio | Statement: [42 (school), hasCampus, 42 Rio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 42 Rio Context triple: [42 (school), hasCampus, 42 Rio]
-
A.
The Carioca
"The Carioca" is a popular 1933 samba-inspired dance song closely associated with the film *Flying Down to Rio* and early Fred Astaire–Ginger Rogers musical numbers.
-
B.
Rion
Rion is a diminutive given name derived from Marion, often used as a shorter or more informal variant.
-
C.
Lucua Osaka
Lucua Osaka is a major modern shopping and dining complex located in the Umeda district of Osaka, Japan.
-
D.
Maracanã River
The Maracanã River is a waterway in Rio de Janeiro, Brazil, that runs through the city near the famous Maracanã Stadium.
-
E.
Río
Río is a central character in the Spanish television series "La Casa de Papel" ("Money Heist"), known as a young, talented hacker and member of the Professor's heist crew.
- 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: 42 Rio Triple: [42 (school), hasCampus, 42 Rio]
Generated description
42 Rio is a tuition-free, peer-to-peer programming school in Rio de Janeiro that follows the innovative, project-based learning model of the international 42 network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 42 Rio Target entity description: 42 Rio is a tuition-free, peer-to-peer programming school in Rio de Janeiro that follows the innovative, project-based learning model of the international 42 network.
-
A.
The Carioca
"The Carioca" is a popular 1933 samba-inspired dance song closely associated with the film *Flying Down to Rio* and early Fred Astaire–Ginger Rogers musical numbers.
-
B.
Rion
Rion is a diminutive given name derived from Marion, often used as a shorter or more informal variant.
-
C.
Lucua Osaka
Lucua Osaka is a major modern shopping and dining complex located in the Umeda district of Osaka, Japan.
-
D.
Maracanã River
The Maracanã River is a waterway in Rio de Janeiro, Brazil, that runs through the city near the famous Maracanã Stadium.
-
E.
Río
Río is a central character in the Spanish television series "La Casa de Papel" ("Money Heist"), known as a young, talented hacker and member of the Professor's heist crew.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbcf11f88190ab1746f973132af1 |
completed | May 3, 2026, 4:15 a.m. |
| NEDg | Description generation | batch_69f6cee0a27081909203e3331186b4ca |
completed | May 3, 2026, 4:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6cf987f68819084edcd6613832fe8 |
completed | May 3, 2026, 4:31 a.m. |
Created at: April 9, 2026, 8:55 p.m.