Triple
T3287600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FX |
E69019
|
entity |
| Predicate | originalProgramming |
P21681
|
FINISHED |
| Object |
Archer
Archer is an animated adult comedy series known for its irreverent spy-themed humor and distinctive visual style, airing on the FX network.
|
E345559
|
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: Archer | Statement: [FX, originalProgramming, Archer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Archer Context triple: [FX, originalProgramming, Archer]
-
A.
Archer
Archer is an English occupational surname historically given to bowmen and now borne by various notable individuals across fields such as music, politics, and sports.
-
B.
Flecher
Flecher is a variant spelling of the surname Fletcher, which traditionally refers to a maker or seller of arrows.
-
C.
Arrow
Arrow is a popular American superhero television series based on the DC Comics character Green Arrow, known for launching the interconnected "Arrowverse" franchise.
-
D.
Arrow
Arrow is the English translation of "Freccia," the nickname of the Italian World War II fighter aircraft Fiat G.50.
-
E.
Graydon Carter
Graydon Carter is a Canadian-born journalist and longtime editor of Vanity Fair, known for his influential role in media and culture.
- 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: Archer Triple: [FX, originalProgramming, Archer]
Generated description
Archer is an animated adult comedy series known for its irreverent spy-themed humor and distinctive visual style, airing on the FX network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Archer Target entity description: Archer is an animated adult comedy series known for its irreverent spy-themed humor and distinctive visual style, airing on the FX network.
-
A.
Archer
Archer is an English occupational surname historically given to bowmen and now borne by various notable individuals across fields such as music, politics, and sports.
-
B.
Flecher
Flecher is a variant spelling of the surname Fletcher, which traditionally refers to a maker or seller of arrows.
-
C.
Arrow
Arrow is a popular American superhero television series based on the DC Comics character Green Arrow, known for launching the interconnected "Arrowverse" franchise.
-
D.
Arrow
Arrow is the English translation of "Freccia," the nickname of the Italian World War II fighter aircraft Fiat G.50.
-
E.
Graydon Carter
Graydon Carter is a Canadian-born journalist and longtime editor of Vanity Fair, known for his influential role in media and culture.
- 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_69ad859d45748190b0742408c954b39f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb058e00881908fdf0a23208860d4 |
completed | March 8, 2026, 5:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2e85f71508190b194b4d383d7ee32 |
completed | March 12, 2026, 4:22 p.m. |
| NEDg | Description generation | batch_69b2e8d165488190bdb6c07257f7502a |
completed | March 12, 2026, 4:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2ecfd3c20819089bc0b2141aee8eb |
completed | March 12, 2026, 4:42 p.m. |
Created at: March 8, 2026, 3:10 p.m.