Triple
T2289490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Back to Basics |
E51469
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Leo da Lion
Leo da Lion is a music producer best known for his work on the project "Back to Basics."
|
E252704
|
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: Leo da Lion | Statement: [Back to Basics, producer, Leo da Lion]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leo da Lion Context triple: [Back to Basics, producer, Leo da Lion]
-
A.
Lion of Brabant
The Lion of Brabant is a historic heraldic emblem representing the Duchy of Brabant and later the broader Brabant region in the Low Countries, often associated with medieval nobility and regional identity.
-
B.
Geoffrey
Geoffrey is a masculine given name of English origin, famously borne by pioneering computer scientist and AI researcher Geoffrey Hinton.
-
C.
David of Sassoun
David of Sassoun is a legendary Armenian folk hero and epic warrior celebrated for his superhuman strength and defense of his homeland in the medieval national epic.
-
D.
Raoul
Raoul is a violent, masked intruder and one of the primary antagonists in the thriller film "Panic Room."
-
E.
Longshanks
Longshanks is the nickname of King Edward I of England, known for his tall stature, military campaigns in Wales and Scotland, and significant legal and administrative reforms.
- 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: Leo da Lion Triple: [Back to Basics, producer, Leo da Lion]
Generated description
Leo da Lion is a music producer best known for his work on the project "Back to Basics."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Leo da Lion Target entity description: Leo da Lion is a music producer best known for his work on the project "Back to Basics."
-
A.
Lion of Brabant
The Lion of Brabant is a historic heraldic emblem representing the Duchy of Brabant and later the broader Brabant region in the Low Countries, often associated with medieval nobility and regional identity.
-
B.
Geoffrey
Geoffrey is a masculine given name of English origin, famously borne by pioneering computer scientist and AI researcher Geoffrey Hinton.
-
C.
David of Sassoun
David of Sassoun is a legendary Armenian folk hero and epic warrior celebrated for his superhuman strength and defense of his homeland in the medieval national epic.
-
D.
Raoul
Raoul is a violent, masked intruder and one of the primary antagonists in the thriller film "Panic Room."
-
E.
Longshanks
Longshanks is the nickname of King Edward I of England, known for his tall stature, military campaigns in Wales and Scotland, and significant legal and administrative reforms.
- 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_69a88b09c644819090b503456d96bf70 |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abc273b67c8190bcd96f9a484647ef |
completed | March 7, 2026, 6:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae7f1e84ac819096cb62ce5e94d865 |
completed | March 9, 2026, 8:04 a.m. |
| NEDg | Description generation | batch_69ae7fee12ac8190bb9924f7467434a6 |
completed | March 9, 2026, 8:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae8061cd348190b0b0b65dcf730f99 |
completed | March 9, 2026, 8:10 a.m. |
Created at: March 4, 2026, 7:48 p.m.