Triple
T14098107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sofala Province |
E339307
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Marromeu
Marromeu is a town and district in central Mozambique known for its location along the Zambezi River and proximity to the Marromeu Buffalo Reserve.
|
E1079928
|
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: Marromeu | Statement: [Sofala Province, contains, Marromeu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marromeu Context triple: [Sofala Province, contains, Marromeu]
-
A.
Putijarra
Putijarra is an Australian Aboriginal language traditionally spoken by the Martu people of the Western Desert region.
-
B.
Morumbi
Morumbi is a major football stadium in São Paulo, Brazil, best known as the home ground of São Paulo FC and a frequent venue for major national and international matches.
-
C.
Mauá
Mauá is an industrial and residential city located in the metropolitan region of São Paulo, Brazil.
-
D.
Serramazzoni
Serramazzoni is a small Italian municipality in the Emilia-Romagna region, known for its hilly Apennine landscape and rural character.
-
E.
Mouraria
Mouraria is a historic Lisbon neighborhood known for its multicultural character, narrow medieval streets, and deep ties to traditional fado music.
- 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: Marromeu Triple: [Sofala Province, contains, Marromeu]
Generated description
Marromeu is a town and district in central Mozambique known for its location along the Zambezi River and proximity to the Marromeu Buffalo Reserve.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marromeu Target entity description: Marromeu is a town and district in central Mozambique known for its location along the Zambezi River and proximity to the Marromeu Buffalo Reserve.
-
A.
Putijarra
Putijarra is an Australian Aboriginal language traditionally spoken by the Martu people of the Western Desert region.
-
B.
Morumbi
Morumbi is a major football stadium in São Paulo, Brazil, best known as the home ground of São Paulo FC and a frequent venue for major national and international matches.
-
C.
Mauá
Mauá is an industrial and residential city located in the metropolitan region of São Paulo, Brazil.
-
D.
Serramazzoni
Serramazzoni is a small Italian municipality in the Emilia-Romagna region, known for its hilly Apennine landscape and rural character.
-
E.
Mouraria
Mouraria is a historic Lisbon neighborhood known for its multicultural character, narrow medieval streets, and deep ties to traditional fado music.
- 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_69d81c69b5c8819094aa1abf18302908 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5fb926288190a7f0f50d1d585d76 |
completed | April 14, 2026, 3:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcd0adfc28819097a1bfd56739c286 |
completed | May 7, 2026, 5:49 p.m. |
| NEDg | Description generation | batch_69fcd41c84408190ab4bc885e7ba8f81 |
completed | May 7, 2026, 6:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fcd4ab4b588190977b3dc2adc1f412 |
completed | May 7, 2026, 6:06 p.m. |
Created at: April 9, 2026, 10:22 p.m.