Triple
T3271039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ponta Delgada |
E68647
|
entity |
| Predicate | hasParish |
P35
|
FINISHED |
| Object |
São José
São José is a civil parish within the municipality of Ponta Delgada on São Miguel Island in the Azores, Portugal.
|
E342599
|
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: São José | Statement: [Ponta Delgada, hasParish, São José]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: São José Context triple: [Ponta Delgada, hasParish, São José]
-
A.
São José
São José is a historic central neighborhood of Recife, Brazil, known for its traditional markets, colonial-era architecture, and vibrant commercial activity.
-
B.
Jundiaí
Jundiaí is a mid-sized industrial and logistics city in southeastern Brazil known for its strong economy and high quality of life.
-
C.
Santo Amaro
Santo Amaro is a central neighborhood in Recife, Brazil, known for its mix of residential areas, commerce, and important urban infrastructure.
-
D.
Cumbuco
Cumbuco is a coastal village in northeastern Brazil known for its sand dunes, lagoons, and strong winds that make it a popular destination for kitesurfing and other beach tourism.
-
E.
Campoalegre
Campoalegre is a municipality and town in southwestern Colombia, located in the Huila Department and known for its agricultural production.
- 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: São José Triple: [Ponta Delgada, hasParish, São José]
Generated description
São José is a civil parish within the municipality of Ponta Delgada on São Miguel Island in the Azores, Portugal.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: São José Target entity description: São José is a civil parish within the municipality of Ponta Delgada on São Miguel Island in the Azores, Portugal.
-
A.
São José
São José is a historic central neighborhood of Recife, Brazil, known for its traditional markets, colonial-era architecture, and vibrant commercial activity.
-
B.
Jundiaí
Jundiaí is a mid-sized industrial and logistics city in southeastern Brazil known for its strong economy and high quality of life.
-
C.
Santo Amaro
Santo Amaro is a central neighborhood in Recife, Brazil, known for its mix of residential areas, commerce, and important urban infrastructure.
-
D.
Cumbuco
Cumbuco is a coastal village in northeastern Brazil known for its sand dunes, lagoons, and strong winds that make it a popular destination for kitesurfing and other beach tourism.
-
E.
Campoalegre
Campoalegre is a municipality and town in southwestern Colombia, located in the Huila Department and known for its agricultural production.
- 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_69ad859b54f881909bf530d549caf2fd |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adaff4b9dc8190b7e3da0bbffccf99 |
completed | March 8, 2026, 5:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28f02a620819091356c965b2aceef |
completed | March 12, 2026, 10:01 a.m. |
| NEDg | Description generation | batch_69b296709f84819094fe9db721a44f83 |
completed | March 12, 2026, 10:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2d6c47e908190b5be5b33da358ade |
completed | March 12, 2026, 3:07 p.m. |
Created at: March 8, 2026, 3:09 p.m.