Triple
T12093139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Calfan |
E287998
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Falcon
"Falcon" is a popular electronic dance music track by French DJ and producer Michael Calfan, known for its uplifting house style and melodic energy.
|
E965637
|
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: Falcon | Statement: [Michael Calfan, notableWork, Falcon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Falcon Context triple: [Michael Calfan, notableWork, Falcon]
-
A.
Falcon
Falcon is a Marvel Comics superhero and member of the Avengers, known for his advanced winged flight suit and partnership with Captain America.
-
B.
Falcon
Falcon is a family of large language models designed for high-performance text generation and widely used in open-source AI applications.
-
C.
Falcon
The Falcon is a bird of prey known for its exceptional speed, keen vision, and use in the sport of falconry.
-
D.
Fighting Falcon
Fighting Falcon is the nickname of the F-16, a widely used American multirole fighter aircraft known for its agility and versatility in combat.
-
E.
Taita falcon
The Taita falcon is a small, rare African bird of prey known for its fast, agile flight and preference for nesting on cliffs in rugged, remote landscapes.
- 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: Falcon Triple: [Michael Calfan, notableWork, Falcon]
Generated description
"Falcon" is a popular electronic dance music track by French DJ and producer Michael Calfan, known for its uplifting house style and melodic energy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Falcon Target entity description: "Falcon" is a popular electronic dance music track by French DJ and producer Michael Calfan, known for its uplifting house style and melodic energy.
-
A.
Falcon
The Falcon is a bird of prey known for its exceptional speed, keen vision, and use in the sport of falconry.
-
B.
Falcon
Falcon is a Marvel Comics superhero and member of the Avengers, known for his advanced winged flight suit and partnership with Captain America.
-
C.
Falcon
Falcon is a family of large language models designed for high-performance text generation and widely used in open-source AI applications.
-
D.
Fighting Falcon
Fighting Falcon is the nickname of the F-16, a widely used American multirole fighter aircraft known for its agility and versatility in combat.
-
E.
Taita falcon
The Taita falcon is a small, rare African bird of prey known for its fast, agile flight and preference for nesting on cliffs in rugged, remote landscapes.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91550ce508190babf5755e1553734 |
completed | April 10, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f66edf7881908f29b5b40b9d020f |
completed | May 2, 2026, 1:04 p.m. |
| NEDg | Description generation | batch_69f5fd79da748190b3f0dd7d7a46314d |
completed | May 2, 2026, 1:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f5feeaf2e48190995f282b02a9caaf |
completed | May 2, 2026, 1:40 p.m. |
Created at: April 8, 2026, 9:48 p.m.