Triple
T14870056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maputo Railway Station |
E349718
|
entity |
| Predicate | locatedInFormer |
P35480
|
FINISHED |
| Object | Lourenço Marques |
E95848
|
NE FINISHED |
How this triple was built (2 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: Lourenço Marques | Statement: [Maputo Railway Station, locatedInFormer, Lourenço Marques]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lourenço Marques Context triple: [Maputo Railway Station, locatedInFormer, Lourenço Marques]
-
A.
Lourenço Marques
chosen
Lourenço Marques is the former name of Maputo, the capital city and main port of Mozambique.
-
B.
Porto Amboim
Porto Amboim is a coastal municipality and port town in western Angola known for its role in regional fishing and maritime trade.
-
C.
Beira
Beira is a major port city in central Mozambique, serving as a key commercial and transport hub for the region.
-
D.
Macapá
Macapá is a Brazilian city located on the northern bank of the Amazon River, known for being one of the few state capitals in the world situated directly on the equator.
-
E.
Beira (Portugal)
Beira is a historical region in central Portugal known for its mountainous landscapes, fortified towns, and role as a traditional territorial division of the country.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded57967748190a7c05ebb74aacb7c |
completed | April 15, 2026, 12:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe651067cc8190b9c218ef1f802762 |
completed | May 8, 2026, 10:34 p.m. |
Created at: April 10, 2026, 1:55 a.m.