Triple
T23521388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dagbani |
E574515
|
entity |
| Predicate | isCloselyRelatedTo |
P10003
|
FINISHED |
| Object | Moore |
—
|
NE NERFINISHED |
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: Moore | Statement: [Dagbani, isCloselyRelatedTo, Moore]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moore Context triple: [Dagbani, isCloselyRelatedTo, Moore]
-
A.
Moore
Moore is a suburban city in central Oklahoma, located just south of Oklahoma City and known for its history of devastating tornadoes.
-
B.
Moore
chosen
Moore is a major Gur language of Burkina Faso and surrounding regions, spoken primarily by the Mossi people.
-
C.
Moore
Moore is a character appearing in the film "Look at Me."
-
D.
Moore
Moore is a small rural town in the Somerset Region of Queensland, Australia, known for its agricultural surroundings and role as a local service centre.
-
E.
Moore
Moore is the middle name of Edward M. Kennedy, the long-serving U.S. senator from Massachusetts and prominent member of the Kennedy political family.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245bb3dcc8190ba9a2b35972b58d0 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1aa873ad48190a86807bd4f26df82 |
completed | April 29, 2026, 6:51 a.m. |
Created at: April 17, 2026, 6:08 p.m.