Triple
T10494246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neta-Lee Hershlag |
E247493
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Leon: The Professional |
E247495
|
NE FINISHED |
How this triple was built (2 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: Leon: The Professional | Statement: [Neta-Lee Hershlag, notableWork, Leon: The Professional]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leon: The Professional Context triple: [Neta-Lee Hershlag, notableWork, Leon: The Professional]
-
A.
Léon: The Professional
chosen
Léon: The Professional is a 1994 crime thriller film by Luc Besson about a hitman who forms an unusual bond with a young girl after her family is murdered.
-
B.
Léon
Léon is a traditional cultural and historical region in northwestern Brittany, France, known for its distinct Breton heritage and coastal landscapes.
-
C.
Léon
Léon is a masculine given name of French origin, commonly used in French-speaking countries and derived from the Latin name Leo, meaning "lion."
-
D.
Léon
Léon is a French surname borne by various notable individuals across fields such as politics, arts, and academia.
-
E.
Lehane
Lehane is the surname of Dennis Lehane, an American novelist known for his crime and mystery fiction, including works like "Mystic River" and the Kenzie-Gennaro series.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5097fe2bc81909d66ce43f3533284 |
completed | April 7, 2026, 1:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d8dcaeb6088190829b6c26eb1de7d5 |
completed | April 10, 2026, 11:19 a.m. |
Created at: April 6, 2026, 12:24 p.m.