Triple
T3096979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angra do Heroísmo |
E64618
|
entity |
| Predicate | hasParish |
P35
|
FINISHED |
| Object |
Santa Luzia
Santa Luzia is a civil parish within the municipality of Angra do Heroísmo on Terceira Island in Portugal’s Azores archipelago.
|
E327502
|
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: Santa Luzia | Statement: [Angra do Heroísmo, hasParish, Santa Luzia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santa Luzia Context triple: [Angra do Heroísmo, hasParish, Santa Luzia]
-
A.
Santa Isabel
Santa Isabel was the colonial capital city of Spanish Equatorial Guinea, serving as the administrative and political center during Spanish rule.
-
B.
Santo Antônio
Santo Antônio is a historic central neighborhood of Recife, Brazil, known for its colonial architecture, commercial activity, and cultural landmarks.
-
C.
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.
-
D.
Sant Anna
Sant Anna is a variant spelling of the Italian name Sant’Anna, commonly referring to places, institutions, or entities named after Saint Anne.
-
E.
Santa Úrsula
Santa Úrsula is a neighborhood in Mexico City best known for hosting the iconic Estadio Azteca football stadium.
- 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: Santa Luzia Triple: [Angra do Heroísmo, hasParish, Santa Luzia]
Generated description
Santa Luzia is a civil parish within the municipality of Angra do Heroísmo on Terceira Island in Portugal’s Azores archipelago.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Santa Luzia Target entity description: Santa Luzia is a civil parish within the municipality of Angra do Heroísmo on Terceira Island in Portugal’s Azores archipelago.
-
A.
Santa Isabel
Santa Isabel was the colonial capital city of Spanish Equatorial Guinea, serving as the administrative and political center during Spanish rule.
-
B.
Santo Antônio
Santo Antônio is a historic central neighborhood of Recife, Brazil, known for its colonial architecture, commercial activity, and cultural landmarks.
-
C.
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.
-
D.
Sant Anna
Sant Anna is a variant spelling of the Italian name Sant’Anna, commonly referring to places, institutions, or entities named after Saint Anne.
-
E.
Santa Úrsula
Santa Úrsula is a neighborhood in Mexico City best known for hosting the iconic Estadio Azteca football stadium.
- 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_69ad857dc98481909e585dc3372e3ed5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada23cbe3c8190b7ec5cfd464a1ca8 |
completed | March 8, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2037483fc8190b8343faa58fb9893 |
completed | March 12, 2026, 12:06 a.m. |
| NEDg | Description generation | batch_69b204a8c5348190a2cb102b08fd6fa5 |
completed | March 12, 2026, 12:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b205bdf5c881908bc6ef7c3c30df65 |
completed | March 12, 2026, 12:15 a.m. |
Created at: March 8, 2026, 3:03 p.m.