Triple
T8431259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Oyelowo |
E199118
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Selma |
E6307
|
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: Selma | Statement: [David Oyelowo, notableWork, Selma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Selma Context triple: [David Oyelowo, notableWork, Selma]
-
A.
Selma
Selma is a feminine given name of Scandinavian origin, notably borne by the Swedish author and Nobel laureate Selma Lagerlöf.
-
B.
Selma
Selma is a historic city in central Alabama best known as a key site of the American civil rights movement, particularly the 1965 Selma-to-Montgomery marches.
-
C.
Selma
Selma is a small agricultural city in California’s San Joaquin Valley, known for its raisin production and location within Fresno County.
-
D.
Selma
Selma is a small suburban city in the San Antonio metropolitan area of south-central Texas, known for its residential communities and proximity to major regional highways and attractions.
-
E.
Selma (2014 film)
chosen
Selma is a 2014 historical drama film that chronicles Martin Luther King Jr.'s leadership in the 1965 voting rights marches from Selma to Montgomery, Alabama.
- 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_69ca8313c99081909a5c6d83b91de5b3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbd1a4876c81908d5a708bb1f35683 |
completed | March 31, 2026, 1:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce0380ed948190bdba247d67769ade |
completed | April 2, 2026, 5:49 a.m. |
Created at: March 30, 2026, 6:07 p.m.