Triple
T2720371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State of São Paulo |
E60066
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Marília
Marília is a mid-sized city in the interior of Brazil known for its food industry, higher education institutions, and role as a regional economic hub.
|
E293509
|
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: Marília | Statement: [State of São Paulo, hasCity, Marília]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marília Context triple: [State of São Paulo, hasCity, Marília]
-
A.
Fernanda Tadeu
Fernanda Tadeu is a Portuguese educator and public figure best known as the wife of former Prime Minister António Costa.
-
B.
Lais Ribeiro
Lais Ribeiro is a Brazilian fashion model best known for her work with Victoria’s Secret and appearances in its high-profile runway shows.
-
C.
Vera Lúcia Cabreira
Vera Lúcia Cabreira was the wife of renowned Brazilian architect Oscar Niemeyer.
-
D.
Yolanda Soares
Yolanda Soares is a Portuguese soprano and crossover singer known for blending classical music with fado and other contemporary styles.
-
E.
Regina Silveira
Regina Silveira is a Brazilian contemporary artist renowned for her conceptual installations and explorations of shadow, perspective, and spatial perception.
- 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: Marília Triple: [State of São Paulo, hasCity, Marília]
Generated description
Marília is a mid-sized city in the interior of Brazil known for its food industry, higher education institutions, and role as a regional economic hub.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marília Target entity description: Marília is a mid-sized city in the interior of Brazil known for its food industry, higher education institutions, and role as a regional economic hub.
-
A.
Fernanda Tadeu
Fernanda Tadeu is a Portuguese educator and public figure best known as the wife of former Prime Minister António Costa.
-
B.
Lais Ribeiro
Lais Ribeiro is a Brazilian fashion model best known for her work with Victoria’s Secret and appearances in its high-profile runway shows.
-
C.
Vera Lúcia Cabreira
Vera Lúcia Cabreira was the wife of renowned Brazilian architect Oscar Niemeyer.
-
D.
Yolanda Soares
Yolanda Soares is a Portuguese soprano and crossover singer known for blending classical music with fado and other contemporary styles.
-
E.
Regina Silveira
Regina Silveira is a Brazilian contemporary artist renowned for her conceptual installations and explorations of shadow, perspective, and spatial perception.
- 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.