Triple
T1961923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Attorney General’s Office (AGO) |
E42605
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | AGO |
E129124
|
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: AGO | Statement: [Attorney General’s Office (AGO), abbreviation, AGO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AGO Context triple: [Attorney General’s Office (AGO), abbreviation, AGO]
-
A.
AGO
chosen
AGO is a major art museum in Toronto, Canada, renowned for its extensive collection of Canadian and international art.
-
B.
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.
-
C.
GA
GA is the official two-letter United States Postal Service abbreviation for the state of Georgia.
-
D.
GA
GA is the IATA airline designator for Garuda Indonesia, the national flag carrier of Indonesia.
-
E.
GA
GA is the abbreviation commonly used for Greater Anglia, a train operating company serving routes in East Anglia and London in the United Kingdom.
- 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_69a88711151c8190940b2572095059d7 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb3aae8c48190afae2cf3af2ae4e9 |
completed | March 7, 2026, 5:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adfbd133908190add1c78d91a813ef |
completed | March 8, 2026, 10:44 p.m. |
Created at: March 4, 2026, 7:36 p.m.