Triple
T13951268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guarujá |
E335529
|
entity |
| Predicate | hasNickname |
P39
|
FINISHED |
| Object |
Pérola do Atlântico
Pérola do Atlântico is a popular Brazilian coastal resort city famed for its scenic beaches and vibrant tourism.
|
E1071512
|
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: Pérola do Atlântico | Statement: [Guarujá, hasNickname, Pérola do Atlântico]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pérola do Atlântico Context triple: [Guarujá, hasNickname, Pérola do Atlântico]
-
A.
Mariana
Mariana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
-
B.
Mariana
"Mariana" is a famous 1851 Pre-Raphaelite painting by John Everett Millais depicting a solitary woman in a richly detailed interior, inspired by Shakespeare’s "Measure for Measure" and Tennyson’s poem of the same name.
-
C.
Mariana
Mariana is a neighborhood (barrio) within the city of Dorado, Puerto Rico.
-
D.
Mariana
Mariana is a historic colonial-era city in the Brazilian state of Minas Gerais, known for its baroque architecture and gold-mining heritage.
-
E.
Baiano
Baiano is a town in Italy’s Campania region that serves as the eastern terminus of the Circumvesuviana railway network connecting communities around Naples.
- 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: Pérola do Atlântico Triple: [Guarujá, hasNickname, Pérola do Atlântico]
Generated description
Pérola do Atlântico is a popular Brazilian coastal resort city famed for its scenic beaches and vibrant tourism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pérola do Atlântico Target entity description: Pérola do Atlântico is a popular Brazilian coastal resort city famed for its scenic beaches and vibrant tourism.
-
A.
Mariana
Mariana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
-
B.
Mariana
"Mariana" is a famous 1851 Pre-Raphaelite painting by John Everett Millais depicting a solitary woman in a richly detailed interior, inspired by Shakespeare’s "Measure for Measure" and Tennyson’s poem of the same name.
-
C.
Mariana
Mariana is a neighborhood (barrio) within the city of Dorado, Puerto Rico.
-
D.
Mariana
Mariana is a historic colonial-era city in the Brazilian state of Minas Gerais, known for its baroque architecture and gold-mining heritage.
-
E.
Baiano
Baiano is a town in Italy’s Campania region that serves as the eastern terminus of the Circumvesuviana railway network connecting communities around Naples.
- 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_69d81c6081b88190b53e317c3370c8fe |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e131c608190b4ffdbada24a3208 |
completed | April 14, 2026, 12:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fba1cca84881909c7733bbc2609eea |
completed | May 6, 2026, 8:17 p.m. |
| NEDg | Description generation | batch_69fba6af4ed881908cb4b79cfa40977c |
completed | May 6, 2026, 8:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fba71a91fc8190b24185994673b33b |
completed | May 6, 2026, 8:39 p.m. |
Created at: April 9, 2026, 10:17 p.m.