Triple
T8079389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aurora |
E188575
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Maria Aurora
Maria Aurora is a landlocked municipality in the province of Aurora in the Philippines, known for its rural landscapes and agricultural economy.
|
E710474
|
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: Maria Aurora | Statement: [Aurora, hasMunicipality, Maria Aurora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maria Aurora Context triple: [Aurora, hasMunicipality, Maria Aurora]
-
A.
Maria Aurora
Maria Aurora was a noted 17th–18th century Swedish noblewoman and courtier, renowned for her beauty, influence, and connections within European royal courts.
-
B.
Rosabella
Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
-
C.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
D.
Béatrix
Béatrix is a novel by Honoré de Balzac that forms part of his larger La Comédie humaine cycle, depicting the complexities of love and society in 19th-century France.
-
E.
Luciana
Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
- 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: Maria Aurora Triple: [Aurora, hasMunicipality, Maria Aurora]
Generated description
Maria Aurora is a landlocked municipality in the province of Aurora in the Philippines, known for its rural landscapes and agricultural economy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maria Aurora Target entity description: Maria Aurora is a landlocked municipality in the province of Aurora in the Philippines, known for its rural landscapes and agricultural economy.
-
A.
Maria Aurora
Maria Aurora was a noted 17th–18th century Swedish noblewoman and courtier, renowned for her beauty, influence, and connections within European royal courts.
-
B.
Rosabella
Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
-
C.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
D.
Béatrix
Béatrix is a novel by Honoré de Balzac that forms part of his larger La Comédie humaine cycle, depicting the complexities of love and society in 19th-century France.
-
E.
Luciana
Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
- 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_69ca82b662e88190b9323daab8c28a21 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb40a3f01c819096a2c9d5d5199fe6 |
completed | March 31, 2026, 3:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc63f79ac08190af49e77bee67921d |
completed | April 1, 2026, 12:16 a.m. |
| NEDg | Description generation | batch_69cc651d340c819089306bac7110f57a |
completed | April 1, 2026, 12:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cc666ecc04819092ee4cc035dde627 |
completed | April 1, 2026, 12:27 a.m. |
Created at: March 30, 2026, 5:28 p.m.