Triple
T6130621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Appling County |
E136707
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Georgia |
E14900
|
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: Georgia | Statement: [Appling County, locatedIn, Georgia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Georgia Context triple: [Appling County, locatedIn, Georgia]
-
A.
Georgia
chosen
Georgia is a southeastern U.S. state known for its diverse landscapes, historic cities like Atlanta and Savannah, and significant roles in both the Civil War and the civil rights movement.
-
B.
Georgia
Georgia is a country at the crossroads of Eastern Europe and Western Asia, known for its ancient culture, mountainous landscapes, and historic role along the Silk Road.
-
C.
Georgia
"Georgia" is a hit single by American rapper Ludacris, known for its soulful hook and homage to the U.S. state of Georgia.
-
D.
Georgia
Georgia is a character from the musical and film "Burlesque," known for her role as one of the performers in the nightclub where the story unfolds.
-
E.
Georgia
Georgia is a 1995 American drama film starring Jennifer Jason Leigh as a struggling singer overshadowed by her more successful sister.
- 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_69c008a0a37c81908e5b4f879158afb3 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05c4de9c48190b98f67a6251ec1df |
completed | March 22, 2026, 9:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c20d56f234819082d89a755ae5a446 |
completed | March 24, 2026, 4:04 a.m. |
Created at: March 22, 2026, 4:15 p.m.