Triple
T7846018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Average Wage Index |
E181924
|
entity |
| Predicate | publisher |
P29
|
FINISHED |
| Object | SSA |
E24298
|
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: SSA | Statement: [National Average Wage Index, publisher, SSA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SSA Context triple: [National Average Wage Index, publisher, SSA]
-
A.
SSA
SSA is the commonly used acronym for Mexico’s federal Secretariat of Health, the government ministry responsible for national public health policy and services.
-
B.
SSA
SSA is the French Armed Forces Health Service, responsible for providing medical support and healthcare to military personnel in France and during overseas operations.
-
C.
SSA
SSA is a professional scientific organization dedicated to advancing the study and understanding of earthquakes and seismic phenomena.
-
D.
SSA
chosen
SSA is the U.S. federal agency responsible for administering Social Security programs, including retirement, disability, and survivors benefits.
-
E.
SAA
SAA is the ICAO airline designator for South African Airways, the flag carrier airline of South Africa.
- 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_69ca8285d6488190a95d4c02d7354b53 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb163f5da08190b7340ef4cc01cd19 |
completed | March 31, 2026, 12:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5af27c9c8190a3e6062d323acff1 |
completed | March 31, 2026, 5:26 a.m. |
Created at: March 30, 2026, 4:49 p.m.