Triple
T253300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Argentina |
E5383
|
entity |
| Predicate | ISO3166-1Alpha3 |
P189
|
FINISHED |
| Object |
ARG
ARG is the three-letter ISO 3166-1 alpha-3 country code that uniquely identifies Argentina in international standards and data systems.
|
E32833
|
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: ARG | Statement: [Argentina, ISO3166-1Alpha3, ARG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ARG Context triple: [Argentina, ISO3166-1Alpha3, ARG]
-
A.
ARC
ARC is the commonly used acronym for the Augmentation Research Center, a pioneering research group known for its early work on interactive computing and human–computer interaction.
-
B.
Ap
Ap is the commonly used abbreviation for the Norwegian Labour Party, a major social-democratic political party in Norway.
-
C.
ATE
ATE is a U.S. National Science Foundation program that supports the education and training of technicians for advanced technology fields through partnerships between two-year colleges, industry, and other educational institutions.
-
D.
AS
AS is the two-letter ISO 3166-1 alpha-2 country code assigned to American Samoa.
-
E.
AS
AS is the official vehicle registration code assigned to the Indian state of Assam.
- 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: ARG Triple: [Argentina, ISO3166-1Alpha3, ARG]
Generated description
ARG is the three-letter ISO 3166-1 alpha-3 country code that uniquely identifies Argentina in international standards and data systems.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ARG Target entity description: ARG is the three-letter ISO 3166-1 alpha-3 country code that uniquely identifies Argentina in international standards and data systems.
-
A.
ARC
ARC is the commonly used acronym for the Augmentation Research Center, a pioneering research group known for its early work on interactive computing and human–computer interaction.
-
B.
Ap
Ap is the commonly used abbreviation for the Norwegian Labour Party, a major social-democratic political party in Norway.
-
C.
ATE
ATE is a U.S. National Science Foundation program that supports the education and training of technicians for advanced technology fields through partnerships between two-year colleges, industry, and other educational institutions.
-
D.
AS
AS is the official vehicle registration code assigned to the Indian state of Assam.
-
E.
AS
AS is the two-letter ISO 3166-1 alpha-2 country code assigned to American Samoa.
- 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_69a2580a64ac8190ad76e34bb0715b5e |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25d5331b48190b3797fece8e60e20 |
completed | Feb. 28, 2026, 3:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3765d90708190891d4fa15616a6b3 |
completed | Feb. 28, 2026, 11:12 p.m. |
| NEDg | Description generation | batch_69a376c686048190aec0abd9c6999663 |
completed | Feb. 28, 2026, 11:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a377aa34cc81908820a5c970d1ecf6 |
completed | Feb. 28, 2026, 11:18 p.m. |
Created at: Feb. 28, 2026, 2:55 a.m.