Triple
T15407102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lafia |
E368488
|
entity |
| Predicate | languageSpoken |
P151
|
FINISHED |
| Object | Alago |
E368489
|
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: Alago | Statement: [Lafia, languageSpoken, Alago]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alago Context triple: [Lafia, languageSpoken, Alago]
-
A.
Alago
chosen
Alago are an ethnic group in central Nigeria, primarily found in Nasarawa State and known for their distinct language and cultural traditions.
-
B.
Norte Grande
Norte Grande is the arid, mineral-rich northern macroregion of Chile that encompasses much of the Atacama Desert and key mining areas.
-
C.
Galoa
Galoa is a small coastal village located on Kadavu Island in Fiji, known for its traditional Fijian lifestyle and surrounding natural beauty.
-
D.
Vidigal
Vidigal is a hillside favela neighborhood in Rio de Janeiro, Brazil, known for its striking ocean views, vibrant community, and growing cultural and tourism scene.
-
E.
Paranhos
Paranhos is a civil parish in the city of Porto, Portugal, known for its residential areas and several university and hospital facilities.
- 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03ea36c6881909eaea48e9608897a |
completed | April 16, 2026, 1:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff135a26f08190ad3fc1d5a263a24e |
completed | May 9, 2026, 10:58 a.m. |
Created at: April 10, 2026, 3:20 a.m.