Triple
T14614137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liang |
E343037
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Leung |
E982037
|
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: Leung | Statement: [Liang, hasVariant, Leung]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leung Context triple: [Liang, hasVariant, Leung]
-
A.
Leung
chosen
Leung is a common Chinese surname, particularly prevalent among Cantonese speakers and notable in Hong Kong and southern China.
-
B.
Lawrence Leung
Lawrence Leung is an Australian comedian, writer, and filmmaker known for his offbeat television series and stand-up shows that blend personal storytelling with nerdy pop-culture obsessions.
-
C.
Shun-Tak Leung
Shun-Tak Leung is a computer scientist known for co-authoring the influential Google File System paper on distributed storage infrastructure at Google.
-
D.
Wing Lei
Wing Lei is an upscale Chinese fine-dining restaurant at Wynn Las Vegas, renowned for its elegant décor and refined Cantonese cuisine.
-
E.
Jimmy Lei Ba
Jimmy Lei Ba is a machine learning researcher known for influential contributions to deep learning optimization and normalization techniques, including the development of Layer Normalization.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb45264988190a1df13e8b54a85bd |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fda92110e88190af47b713dd24520b |
completed | May 8, 2026, 9:13 a.m. |
Created at: April 10, 2026, 1:25 a.m.