Triple
T9728144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Ladysmith |
E235666
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Natal |
E15763
|
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: Natal | Statement: [Battle of Ladysmith, location, Natal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Natal Context triple: [Battle of Ladysmith, location, Natal]
-
A.
Natal
chosen
Natal is a historical region in southeastern South Africa, centered on the port city of Durban and known for its colonial history and diverse cultural heritage.
-
B.
Natal
Natal is a coastal city in northeastern Brazil known for its beaches, sand dunes, and role as a regional tourism and economic hub.
-
C.
Neive
Neive is a picturesque medieval village in Italy’s Piedmont wine region, renowned for its historic charm and production of Barbaresco and other Langhe wines.
-
D.
Nannini
Nannini is an Italian surname most prominently associated with rock singer-songwriter Gianna Nannini and her family.
-
E.
Nicia
Nicia is a foolish and gullible Florentine lawyer who serves as one of the central comic figures in Niccolò Machiavelli’s play "The Mandrake."
- 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_69ca84d0fad481909cdd45aa77416c48 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9e7af544819090a8a1adec41943c |
completed | April 1, 2026, 10:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d19fb208a48190864f8f085da83db7 |
completed | April 4, 2026, 11:33 p.m. |
Created at: March 30, 2026, 8:21 p.m.