Triple
T16981616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Les Kelley |
E411958
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Les |
E762
|
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: Les | Statement: [Les Kelley, hasGivenName, Les]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Les Context triple: [Les Kelley, hasGivenName, Les]
-
A.
Les
Les is a small municipality in the Aran Valley of Catalonia, Spain, known for its Pyrenean mountain setting and traditional local culture.
-
B.
Le
Le is a common Vietnamese surname shared by many notable figures in the country’s history and culture.
-
C.
Lee
Lee is a residential district in southeast London known for its suburban character, green spaces, and Victorian and Edwardian housing.
-
D.
Lee
chosen
Lee is a given name shared by numerous individuals across different cultures and professions.
-
E.
Letz
Letz is the surname of George Montgomery, an American actor and filmmaker active in mid-20th-century Hollywood.
- 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_69d886ca8f348190812768ea8d5055ce |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d18830ac8190a20c89a87379ae94 |
completed | April 18, 2026, 6:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00d479610c8190a6281e6d4959b820 |
completed | May 10, 2026, 6:54 p.m. |
Created at: April 10, 2026, 5:32 a.m.