Triple
T17559398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fast Application Notification |
E427660
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
FAN
FAN is an acronym for Fast Application Notification, a system or mechanism designed to quickly deliver alerts or updates from applications.
|
E1275575
|
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: FAN | Statement: [Fast Application Notification, alsoKnownAs, FAN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FAN Context triple: [Fast Application Notification, alsoKnownAs, FAN]
-
A.
FAN
FAN is the French acronym for Niger's national military, responsible for the country's defense and security operations.
-
B.
FAN
FAN was a key rebel armed group in Chad that played a major role in the country’s internal conflicts during the late 20th century.
-
C.
Fanfan
Fanfan is a romantic film best known for starring French actress Sophie Marceau.
-
D.
FAAN
FAAN is the government agency responsible for managing and operating commercial airports and related aviation services across Nigeria.
-
E.
FAAN
FAAN is a post-nominal credential signifying election as a Fellow of the American Academy of Neurology, recognizing distinguished contributions to the field of neurology.
- 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: FAN Triple: [Fast Application Notification, alsoKnownAs, FAN]
Generated description
FAN is an acronym for Fast Application Notification, a system or mechanism designed to quickly deliver alerts or updates from applications.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: FAN Target entity description: FAN is an acronym for Fast Application Notification, a system or mechanism designed to quickly deliver alerts or updates from applications.
-
A.
FAN
FAN is the French acronym for Niger's national military, responsible for the country's defense and security operations.
-
B.
FAN
FAN was a key rebel armed group in Chad that played a major role in the country’s internal conflicts during the late 20th century.
-
C.
Fanfan
Fanfan is a romantic film best known for starring French actress Sophie Marceau.
-
D.
FAAN
FAAN is a post-nominal credential signifying election as a Fellow of the American Academy of Neurology, recognizing distinguished contributions to the field of neurology.
-
E.
FAAN
FAAN is the government agency responsible for managing and operating commercial airports and related aviation services across Nigeria.
- 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_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4562573e48190a19f30fe915a5455 |
completed | April 19, 2026, 4:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01d29694a8819095351b929cfaf324 |
completed | May 11, 2026, 12:59 p.m. |
| NEDg | Description generation | batch_6a01d4436b448190bff8710eac0ce569 |
completed | May 11, 2026, 1:06 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01d49cb9c081908b07b87ec3b2a133 |
completed | May 11, 2026, 1:07 p.m. |
Created at: April 10, 2026, 5:50 a.m.