Triple
T18873940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AGN Agreement |
E461636
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
AGN
AGN is a shorthand designation commonly used to refer to the AGN Agreement.
|
E1347576
|
NE FINISHED |
How this triple was built (4 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: AGN | Statement: [AGN Agreement, shortName, AGN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AGN Context triple: [AGN Agreement, shortName, AGN]
-
A.
AGN
AGN is the stock ticker symbol for Aegon, a multinational life insurance, pensions, and asset management company headquartered in the Netherlands.
-
B.
AGN
AGN is the acronym commonly used for the Archivo General de la Nación, the national archive institution responsible for preserving and managing a country's historical and governmental documents.
-
C.
Blazar
Blazar is the OpenStack reservation service that enables users to book cloud resources such as compute, storage, and networking in advance for guaranteed availability.
-
D.
AGA
AGA is the Advanced Graphics Architecture chipset used in later Commodore Amiga computers, providing enhanced color depth and graphics capabilities over earlier Amiga chipsets.
-
E.
AGA
AGA is a Swedish industrial gas and engineering company historically known for its innovations in lighthouse technology and gas appliances.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: AGN Triple: [AGN Agreement, shortName, AGN]
Generated description
AGN is a shorthand designation commonly used to refer to the AGN Agreement.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: AGN Target entity description: AGN is a shorthand designation commonly used to refer to the AGN Agreement.
-
A.
AGN
AGN is the stock ticker symbol for Aegon, a multinational life insurance, pensions, and asset management company headquartered in the Netherlands.
-
B.
AGN
AGN is the acronym commonly used for the Archivo General de la Nación, the national archive institution responsible for preserving and managing a country's historical and governmental documents.
-
C.
Blazar
Blazar is the OpenStack reservation service that enables users to book cloud resources such as compute, storage, and networking in advance for guaranteed availability.
-
D.
AGA
AGA is the Advanced Graphics Architecture chipset used in later Commodore Amiga computers, providing enhanced color depth and graphics capabilities over earlier Amiga chipsets.
-
E.
AGA
AGA is a Swedish industrial gas and engineering company historically known for its innovations in lighthouse technology and gas appliances.
- F. None of above. chosen
Provenance (5 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_69d8dcfc3430819095ee6fc0eb4c06a5 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c2ab4bb4819086ae98a38dd9b11b |
completed | April 20, 2026, 6:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0575bfc9988190bc72525ab30cf498 |
completed | May 14, 2026, 7:12 a.m. |
| NEDg | Description generation | batch_6a057803e99481909c6ed82014a169d0 |
completed | May 14, 2026, 7:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05785d1e08819097299cf6d9e90035 |
completed | May 14, 2026, 7:23 a.m. |
Created at: April 10, 2026, 11:57 a.m.