Triple
T18300781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Farama Foundation |
E438351
|
entity |
| Predicate | product |
P490
|
FINISHED |
| Object |
Farama-Notifications
Farama-Notifications is a tool or service from the Farama Foundation designed to deliver updates and alerts related to its reinforcement learning ecosystems and projects.
|
E1317493
|
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: Farama-Notifications | Statement: [Farama Foundation, product, Farama-Notifications]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Farama-Notifications Context triple: [Farama Foundation, product, Farama-Notifications]
-
A.
FAMO
FAMO was a German vehicle manufacturer best known for producing military half-tracks and armored vehicles for the Wehrmacht during World War II.
-
B.
FAY
FAY is the three-letter FAA airport code for Fayetteville Regional Airport serving Fayetteville, North Carolina.
-
C.
FAM
FAM is the national governing body responsible for overseeing and developing football in Malaysia.
-
D.
FAM
FAM is the acronym commonly used to refer to the Mexican Air Force, the aerial warfare branch of Mexico’s armed forces.
-
E.
FAKM
FAKM is the ICAO airport code assigned to Kimberley Airport in South Africa.
- 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: Farama-Notifications Triple: [Farama Foundation, product, Farama-Notifications]
Generated description
Farama-Notifications is a tool or service from the Farama Foundation designed to deliver updates and alerts related to its reinforcement learning ecosystems and projects.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Farama-Notifications Target entity description: Farama-Notifications is a tool or service from the Farama Foundation designed to deliver updates and alerts related to its reinforcement learning ecosystems and projects.
-
A.
FAMO
FAMO was a German vehicle manufacturer best known for producing military half-tracks and armored vehicles for the Wehrmacht during World War II.
-
B.
FAY
FAY is the three-letter FAA airport code for Fayetteville Regional Airport serving Fayetteville, North Carolina.
-
C.
FAM
FAM is the national governing body responsible for overseeing and developing football in Malaysia.
-
D.
FAM
FAM is the acronym commonly used to refer to the Mexican Air Force, the aerial warfare branch of Mexico’s armed forces.
-
E.
FAKM
FAKM is the ICAO airport code assigned to Kimberley Airport in South Africa.
- 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_69d8b915e3e881909125d760c15d0c29 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5017f63dc819083a675d570620f2f |
completed | April 19, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03bb5e1fb481908a0b98ea130eda71 |
completed | May 12, 2026, 11:44 p.m. |
| NEDg | Description generation | batch_6a03bdb3fb3c819095192ac49e809f55 |
completed | May 12, 2026, 11:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03c193a0a08190b33d80d45f3ed0f0 |
completed | May 13, 2026, 12:10 a.m. |
Created at: April 10, 2026, 10:35 a.m.