Triple
T2720369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State of São Paulo |
E60066
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Suzano
Suzano is a municipality in the eastern part of the São Paulo metropolitan region in Brazil, known for its industrial activity and integration into Greater São Paulo’s urban area.
|
E293507
|
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: Suzano | Statement: [State of São Paulo, hasCity, Suzano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suzano Context triple: [State of São Paulo, hasCity, Suzano]
-
A.
UPM
UPM is the Polytechnic University of Madrid, a leading Spanish public university specializing in engineering, architecture, and technology.
-
B.
Pão de Açúcar
Pão de Açúcar is the iconic granite peak at the entrance of Guanabara Bay in Rio de Janeiro, Brazil, famous for its panoramic cable car views of the city and coastline.
-
C.
Branobel
Branobel was a major late-19th-century oil company based in the Russian Empire, founded by members of the Nobel family and influential in the early global petroleum industry.
-
D.
Borregaard
Borregaard is a Norwegian biorefinery company that produces advanced and sustainable bio-based chemicals and materials from wood.
-
E.
Mibuchi
Mibuchi is a Japanese surname borne by individuals such as Tadahiko Mibuchi.
- 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: Suzano Triple: [State of São Paulo, hasCity, Suzano]
Generated description
Suzano is a municipality in the eastern part of the São Paulo metropolitan region in Brazil, known for its industrial activity and integration into Greater São Paulo’s urban area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Suzano Target entity description: Suzano is a municipality in the eastern part of the São Paulo metropolitan region in Brazil, known for its industrial activity and integration into Greater São Paulo’s urban area.
-
A.
UPM
UPM is the Polytechnic University of Madrid, a leading Spanish public university specializing in engineering, architecture, and technology.
-
B.
Pão de Açúcar
Pão de Açúcar is the iconic granite peak at the entrance of Guanabara Bay in Rio de Janeiro, Brazil, famous for its panoramic cable car views of the city and coastline.
-
C.
Branobel
Branobel was a major late-19th-century oil company based in the Russian Empire, founded by members of the Nobel family and influential in the early global petroleum industry.
-
D.
Borregaard
Borregaard is a Norwegian biorefinery company that produces advanced and sustainable bio-based chemicals and materials from wood.
-
E.
Mibuchi
Mibuchi is a Japanese surname borne by individuals such as Tadahiko Mibuchi.
- 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_69ab4b746d248190958e052045c09255 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdab06d388190acf690787fe58ab5 |
completed | March 7, 2026, 7:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afb6914f70819099482893d026f34b |
completed | March 10, 2026, 6:13 a.m. |
| NEDg | Description generation | batch_69afb726182081909570e4cb7a364e4d |
completed | March 10, 2026, 6:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afb78f9d08819087d6f31fe1e4e61c |
completed | March 10, 2026, 6:17 a.m. |
Created at: March 6, 2026, 9:55 p.m.