Triple
T16824849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baxley, Georgia |
E408991
|
entity |
| Predicate | stateAbbreviation |
P2461
|
FINISHED |
| Object | GA |
E82200
|
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: GA | Statement: [Baxley, Georgia, stateAbbreviation, GA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GA Context triple: [Baxley, Georgia, stateAbbreviation, GA]
-
A.
GA
GA is the commonly used abbreviation for the United Nations General Assembly, the main deliberative body of the UN where all member states are represented.
-
B.
GA
chosen
GA is the official two-letter United States Postal Service abbreviation for the state of Georgia.
-
C.
GA
GA is the IATA airline designator for Garuda Indonesia, the national flag carrier of Indonesia.
-
D.
GA
GA is the vehicle registration code assigned to the Indian state of Goa.
-
E.
GA
GA is the commonly used abbreviation for General Atomics, an American energy and defense corporation known for its work in nuclear technology and unmanned aerial vehicles.
- 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_69d88394566c8190b3dcbdc72935f7fa |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b310ffec81908087e5aaacc4a7c2 |
completed | April 18, 2026, 4:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b29c170c81908fcc88c31e266ffb |
completed | May 10, 2026, 4:30 p.m. |
Created at: April 10, 2026, 5:23 a.m.