Triple
T23374223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lynn Whitfield |
E593560
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Congo |
E1570954
|
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: Congo | Statement: [Lynn Whitfield, notableWork, Congo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Congo Context triple: [Lynn Whitfield, notableWork, Congo]
-
A.
Congo
Congo is a Central African country whose economy is heavily reliant on oil production and exports.
-
B.
Congo
"Congo" is a 1997 rock single by the British band Genesis, released as the lead track from their album "Calling All Stations."
-
C.
Congo
chosen
"Congo" is a 1995 science fiction adventure film, based on Michael Crichton's novel, about an expedition into the African jungle that encounters deadly mysteries and advanced technology.
-
D.
Kongō
Kongō was a Japanese Kongō-class fast battleship that served prominently in the Imperial Japanese Navy during World War II.
-
E.
Kongo
Kongo refers to the Central African ethnic and cultural group and historical kingdom whose traditions and beliefs have significantly influenced Afro-diasporic religions in the Americas.
- 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_69e25d268a50819095f2fd479da8ef3f |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1a3b1d24881909945936cbf00876e |
completed | April 29, 2026, 6:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c5ddfbbf0819098166b60f5e5e62e |
completed | May 19, 2026, 12:55 p.m. |
Created at: April 17, 2026, 5:33 p.m.