Triple
T2720705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | São Paulo FC |
E60072
|
entity |
| Predicate | derby |
P3425
|
FINISHED |
| Object |
San-São
San-São is the traditional Brazilian football derby between São Paulo FC and Santos FC, known for its historic rivalries and memorable matches.
|
E293547
|
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: San-São | Statement: [São Paulo FC, derby, San-São]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San-São Context triple: [São Paulo FC, derby, San-São]
-
A.
Cardoso
Cardoso is a common Portuguese-language surname borne by numerous individuals, including prominent Brazilian political and cultural figures.
-
B.
Santos-o-Velho
Santos-o-Velho is a historic riverside neighborhood in Lisbon, Portugal, known for its old convents, palaces, and vibrant nightlife along the Tagus.
-
C.
Santa Cruz do Sul
Santa Cruz do Sul is a city in southern Brazil known for its strong German-Brazilian cultural heritage, architecture, and traditions.
-
D.
A Serrana
A Serrana is a Portuguese opera composed by Alfredo Keil, recognized as one of the significant works in 19th-century Portuguese musical theater.
-
E.
Chiado
Chiado is a historic and upscale neighborhood in central Lisbon known for its elegant shops, cafés, theaters, and literary heritage.
- 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: San-São Triple: [São Paulo FC, derby, San-São]
Generated description
San-São is the traditional Brazilian football derby between São Paulo FC and Santos FC, known for its historic rivalries and memorable matches.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: San-São Target entity description: San-São is the traditional Brazilian football derby between São Paulo FC and Santos FC, known for its historic rivalries and memorable matches.
-
A.
Cardoso
Cardoso is a common Portuguese-language surname borne by numerous individuals, including prominent Brazilian political and cultural figures.
-
B.
Santos-o-Velho
Santos-o-Velho is a historic riverside neighborhood in Lisbon, Portugal, known for its old convents, palaces, and vibrant nightlife along the Tagus.
-
C.
Santa Cruz do Sul
Santa Cruz do Sul is a city in southern Brazil known for its strong German-Brazilian cultural heritage, architecture, and traditions.
-
D.
A Serrana
A Serrana is a Portuguese opera composed by Alfredo Keil, recognized as one of the significant works in 19th-century Portuguese musical theater.
-
E.
Chiado
Chiado is a historic and upscale neighborhood in central Lisbon known for its elegant shops, cafés, theaters, and literary heritage.
- 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.