Triple
T3345303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Army Africa |
E70358
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
SETAF-AF
SETAF-AF is the U.S. Army’s component command for Africa, responsible for Army operations, security cooperation, and partnership activities across the African continent.
|
E350947
|
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: SETAF-AF | Statement: [United States Army Africa, alsoKnownAs, SETAF-AF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SETAF-AF Context triple: [United States Army Africa, alsoKnownAs, SETAF-AF]
-
A.
SAF
SAF is the three-letter IATA airport code for Santa Fe Regional Airport in Santa Fe, New Mexico.
-
B.
AEF
AEF was the common abbreviation for French Equatorial Africa, a former federation of French colonial territories in central Africa.
-
C.
KSAF
KSAF is the ICAO airport code for Santa Fe Regional Airport, a public airport serving Santa Fe, New Mexico.
-
D.
ASFAR
ASFAR is a prominent Moroccan football club based in Rabat, known for its strong domestic record and association with the Royal Moroccan Armed Forces.
-
E.
al-Safa
Al-Safa is one of the two small hills inside the Masjid al-Haram in Mecca between which Muslims perform the ritual walk (sa'i) during Hajj and Umrah.
- 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: SETAF-AF Triple: [United States Army Africa, alsoKnownAs, SETAF-AF]
Generated description
SETAF-AF is the U.S. Army’s component command for Africa, responsible for Army operations, security cooperation, and partnership activities across the African continent.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SETAF-AF Target entity description: SETAF-AF is the U.S. Army’s component command for Africa, responsible for Army operations, security cooperation, and partnership activities across the African continent.
-
A.
SAF
SAF is the three-letter IATA airport code for Santa Fe Regional Airport in Santa Fe, New Mexico.
-
B.
AEF
AEF was the common abbreviation for French Equatorial Africa, a former federation of French colonial territories in central Africa.
-
C.
KSAF
KSAF is the ICAO airport code for Santa Fe Regional Airport, a public airport serving Santa Fe, New Mexico.
-
D.
ASFAR
ASFAR is a prominent Moroccan football club based in Rabat, known for its strong domestic record and association with the Royal Moroccan Armed Forces.
-
E.
al-Safa
Al-Safa is one of the two small hills inside the Masjid al-Haram in Mecca between which Muslims perform the ritual walk (sa'i) during Hajj and Umrah.
- 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_69ad85a405e48190b6e68de7cf9f319e |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb1f36c74819093ef2c74a46c2351 |
completed | March 8, 2026, 5:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3251d49d08190b74483b69024acff |
completed | March 12, 2026, 8:42 p.m. |
| NEDg | Description generation | batch_69b326a29cbc8190a5ae5fd5851ed0c7 |
completed | March 12, 2026, 8:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3270962648190925b04e44542c9c9 |
completed | March 12, 2026, 8:50 p.m. |
Created at: March 8, 2026, 3:12 p.m.