Triple
T14098103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sofala Province |
E339307
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Dondo
Dondo is a town and district in central Mozambique known as an important railway and industrial hub near the port city of Beira in Sofala Province.
|
E1079925
|
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: Dondo | Statement: [Sofala Province, contains, Dondo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dondo Context triple: [Sofala Province, contains, Dondo]
-
A.
Dondo
Dondo is an Austronesian language spoken in Central Sulawesi, Indonesia, belonging to the Tomini–Tolitoli subgroup.
-
B.
Dongo
Dongo is a small town on the northwestern shore of Lake Como in Lombardy, Italy, known for its role in the capture of Benito Mussolini at the end of World War II.
-
C.
Datooga
Datooga is a Southern Nilotic language spoken primarily by the Datooga people of north-central Tanzania.
-
D.
Konda-Dora
Konda-Dora is a South-Central Dravidian language spoken primarily by the Konda Dora tribal community in parts of Andhra Pradesh and Odisha, India.
-
E.
Chivi
Chivi is a rural district and settlement in southern Zimbabwe known for its communal farming communities and semi-arid landscape within Masvingo Province.
- 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: Dondo Triple: [Sofala Province, contains, Dondo]
Generated description
Dondo is a town and district in central Mozambique known as an important railway and industrial hub near the port city of Beira in Sofala Province.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dondo Target entity description: Dondo is a town and district in central Mozambique known as an important railway and industrial hub near the port city of Beira in Sofala Province.
-
A.
Dondo
Dondo is an Austronesian language spoken in Central Sulawesi, Indonesia, belonging to the Tomini–Tolitoli subgroup.
-
B.
Dongo
Dongo is a small town on the northwestern shore of Lake Como in Lombardy, Italy, known for its role in the capture of Benito Mussolini at the end of World War II.
-
C.
Datooga
Datooga is a Southern Nilotic language spoken primarily by the Datooga people of north-central Tanzania.
-
D.
Konda-Dora
Konda-Dora is a South-Central Dravidian language spoken primarily by the Konda Dora tribal community in parts of Andhra Pradesh and Odisha, India.
-
E.
Chivi
Chivi is a rural district and settlement in southern Zimbabwe known for its communal farming communities and semi-arid landscape within Masvingo Province.
- 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.