Triple
T7771712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ma Sichun |
E179087
|
entity |
| Predicate | socialMediaPlatform |
P57
|
FINISHED |
| Object |
E218224
|
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: Weibo | Statement: [Ma Sichun, socialMediaPlatform, Weibo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Weibo Context triple: [Ma Sichun, socialMediaPlatform, Weibo]
-
A.
Weibo
chosen
Weibo is a major Chinese microblogging and social media platform widely used for news, entertainment, and public discourse.
-
B.
WeChat
WeChat is a Chinese multi-purpose mobile app developed by Tencent that combines messaging, social media, and payment services into a single platform.
-
C.
SNS
SNS is the National Rail station code for Staines railway station in Surrey, England.
-
D.
SNS
SNS is the School of Natural Sciences at the University of California, Merced, which focuses on education and research in scientific disciplines such as biology, chemistry, physics, and related fields.
-
E.
Sina
Sina is a given name and surname used in various cultures, often associated with notable figures in fields such as science, arts, and media.
- 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_69c69f30602c819082ab52cd4af5c592 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c7045ebae88190a04c8f972795e615 |
completed | March 27, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8c7ead45c8190847365d8549eefab |
completed | March 29, 2026, 6:34 a.m. |
Created at: March 27, 2026, 4:11 p.m.