Triple
T14361533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LFW |
E356114
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Lomé, Togo |
E71688
|
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: Lomé, Togo | Statement: [LFW, location, Lomé, Togo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lomé, Togo Context triple: [LFW, location, Lomé, Togo]
-
A.
Lomé
chosen
Lomé is the coastal capital and largest city of Togo, serving as a key economic and cultural hub in West Africa.
-
B.
Togo Terminal
Togo Terminal is a container terminal operator managing cargo handling and logistics services at the Port of Lomé in Togo.
-
C.
Serekunda
Serekunda is the most populous urban center and a major commercial hub in The Gambia.
-
D.
Libreville
Libreville is the largest city and main economic and cultural center of Gabon, located on the country’s Atlantic coast.
-
E.
Port-Gentil
Port-Gentil is Gabon's second-largest city and a major oil and port hub located on the country's Atlantic coast.
- 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_69d82790a7e08190877e2d349b2e8d8e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8fabec088190bd8128371b29e958 |
completed | April 14, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd94a018c88190b4fdbf36687ad973 |
completed | May 8, 2026, 7:45 a.m. |
Created at: April 10, 2026, 1:15 a.m.