Triple
T15496412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julianne Nicholson |
E378828
|
entity |
| Predicate | film |
P9968
|
FINISHED |
| Object | Togo |
E1168115
|
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: Togo | Statement: [Julianne Nicholson, film, Togo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Togo Context triple: [Julianne Nicholson, film, Togo]
-
A.
Togo
chosen
Togo is a 2019 historical adventure drama film that tells the true story of a sled dog and his musher leading a perilous serum run across Alaska.
-
B.
Togo
Togo is a small West African country on the Gulf of Guinea, known for its diverse cultures, coastal capital Lomé, and history as a former French colony.
-
C.
Benin
Benin is a West African country on the Gulf of Guinea known for its historical Kingdom of Dahomey and as a key region in the transatlantic slave trade.
-
D.
Burkina Faso
Burkina Faso is a landlocked West African country known for its diverse cultures, Sahelian landscapes, and capital city, Ouagadougou.
-
E.
Gabon
Gabon is a Central African country on the Atlantic coast, known for its equatorial rainforests, rich biodiversity, and significant oil reserves.
- 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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03faecd60819091eeaa56c9c8f67d |
completed | April 16, 2026, 1:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff6780ee3081908a0a833d887b1829 |
completed | May 9, 2026, 4:57 p.m. |
Created at: April 10, 2026, 3:52 a.m.