Triple
T3608737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Centro Region |
E76433
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Trancoso
Trancoso is a historic Portuguese town in the Centro Region, known for its medieval walls, castle, and well-preserved old quarter.
|
E374160
|
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: Trancoso | Statement: [Centro Region, containsCity, Trancoso]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trancoso Context triple: [Centro Region, containsCity, Trancoso]
-
A.
Rocha
Rocha is a Portuguese-origin surname common in Lusophone countries and among their diasporas.
-
B.
Congonhas
Congonhas is a district in the city of São Paulo, Brazil, best known for giving its name to one of the country’s busiest domestic airports.
-
C.
Cardoso
Cardoso is a common Portuguese-language surname borne by numerous individuals, including prominent Brazilian political and cultural figures.
-
D.
Caieiras
Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
-
E.
Caicó
Caicó is a municipality in the interior of Rio Grande do Norte, Brazil, known for its strong cultural traditions, especially its famous religious festivals and regional cuisine.
- 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: Trancoso Triple: [Centro Region, containsCity, Trancoso]
Generated description
Trancoso is a historic Portuguese town in the Centro Region, known for its medieval walls, castle, and well-preserved old quarter.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Trancoso Target entity description: Trancoso is a historic Portuguese town in the Centro Region, known for its medieval walls, castle, and well-preserved old quarter.
-
A.
Rocha
Rocha is a Portuguese-origin surname common in Lusophone countries and among their diasporas.
-
B.
Congonhas
Congonhas is a district in the city of São Paulo, Brazil, best known for giving its name to one of the country’s busiest domestic airports.
-
C.
Cardoso
Cardoso is a common Portuguese-language surname borne by numerous individuals, including prominent Brazilian political and cultural figures.
-
D.
Caieiras
Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
-
E.
Caicó
Caicó is a municipality in the interior of Rio Grande do Norte, Brazil, known for its strong cultural traditions, especially its famous religious festivals and regional cuisine.
- 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_69ad85da0ba481908b3b48c69efe2b98 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc22a3cf081908c20b6fb55be0db2 |
completed | March 8, 2026, 6:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4330de7a08190933aa7e9dc0a65be |
completed | March 13, 2026, 3:53 p.m. |
| NEDg | Description generation | batch_69b437cf839881909b1d505328285123 |
completed | March 13, 2026, 4:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b43835994c81909230bbb21b12b8ef |
completed | March 13, 2026, 4:15 p.m. |
Created at: March 8, 2026, 3:22 p.m.