Triple
T14158084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nampula Province |
E350864
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Moma
Moma is a coastal town and district in northern Mozambique’s Nampula Province, known for its fishing communities and proximity to mineral-rich areas.
|
E1083818
|
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: Moma | Statement: [Nampula Province, hasCity, Moma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moma Context triple: [Nampula Province, hasCity, Moma]
-
A.
Museon
Museon is a science and culture museum in The Hague, Netherlands, known for its interactive exhibits on nature, technology, and world cultures.
-
B.
Muzeum
Muzeum is a major interchange station in the Prague Metro system, located beneath Wenceslas Square and serving as a key hub for lines A and C.
-
C.
Museum of Us
The Museum of Us is an anthropology and cultural history museum in San Diego that explores human experiences, beliefs, and diversity across time and cultures.
-
D.
El Mogamma
El Mogamma is a massive government administrative complex in Cairo’s Tahrir Square, long known as the central hub of Egypt’s bureaucratic paperwork and public services.
-
E.
Moross
Moross is a surname most notably associated with American composer Jerome Moross, known for his film and television scores and concert works.
- 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: Moma Triple: [Nampula Province, hasCity, Moma]
Generated description
Moma is a coastal town and district in northern Mozambique’s Nampula Province, known for its fishing communities and proximity to mineral-rich areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Moma Target entity description: Moma is a coastal town and district in northern Mozambique’s Nampula Province, known for its fishing communities and proximity to mineral-rich areas.
-
A.
Museon
Museon is a science and culture museum in The Hague, Netherlands, known for its interactive exhibits on nature, technology, and world cultures.
-
B.
Muzeum
Muzeum is a major interchange station in the Prague Metro system, located beneath Wenceslas Square and serving as a key hub for lines A and C.
-
C.
Museum of Us
The Museum of Us is an anthropology and cultural history museum in San Diego that explores human experiences, beliefs, and diversity across time and cultures.
-
D.
El Mogamma
El Mogamma is a massive government administrative complex in Cairo’s Tahrir Square, long known as the central hub of Egypt’s bureaucratic paperwork and public services.
-
E.
Moross
Moross is a surname most notably associated with American composer Jerome Moross, known for his film and television scores and concert works.
- 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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61377de48190a3470d28f0edd34a |
completed | April 14, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcf7ef4d80819098d210503f5d22e9 |
completed | May 7, 2026, 8:37 p.m. |
| NEDg | Description generation | batch_69fd02cee5e0819086718893d1621481 |
completed | May 7, 2026, 9:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd063668f4819099d52bee7e7cdc32 |
completed | May 7, 2026, 9:37 p.m. |
Created at: April 10, 2026, 12:58 a.m.