Triple
T15841518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Venezuelan Armed Forces |
E384112
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object |
FANB
FANB is the official acronym for Venezuela’s national military institution, encompassing its army, navy, air force, and related defense components.
|
E1180543
|
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: FANB | Statement: [Venezuelan Armed Forces, abbreviation, FANB]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FANB Context triple: [Venezuelan Armed Forces, abbreviation, FANB]
-
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.
FAN-nee
FAN-nee is the stress pattern indicating that the primary emphasis falls on the first syllable of the name “Fannie.”
-
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: FANB Triple: [Venezuelan Armed Forces, abbreviation, FANB]
Generated description
FANB is the official acronym for Venezuela’s national military institution, encompassing its army, navy, air force, and related defense components.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: FANB Target entity description: FANB is the official acronym for Venezuela’s national military institution, encompassing its army, navy, air force, and related defense components.
-
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.
FAN-nee
FAN-nee is the stress pattern indicating that the primary emphasis falls on the first syllable of the name “Fannie.”
-
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_69d86da34c888190976e06c4019d415a |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e142e764088190afba6c5c1acf8199 |
completed | April 16, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa13eab4881908794104508ba53af |
completed | May 9, 2026, 9:03 p.m. |
| NEDg | Description generation | batch_69ffa527af048190b1f87d85e50bf254 |
completed | May 9, 2026, 9:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffa5df00e481909e203e78940395ed |
completed | May 9, 2026, 9:23 p.m. |
Created at: April 10, 2026, 4:50 a.m.