Triple
T14423147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naver Corporation |
E357631
|
entity |
| Predicate | operates |
P24
|
FINISHED |
| Object | LINE |
E697175
|
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: LINE | Statement: [Naver Corporation, operates, LINE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LINE Context triple: [Naver Corporation, operates, LINE]
-
A.
LINE
chosen
LINE is a popular Japanese messaging and social media platform that offers free calls, chats, stickers, and various integrated services across mobile and desktop devices.
-
B.
Business Line
Business Line is an Indian business and financial daily newspaper known for its coverage of markets, economy, and corporate affairs.
-
C.
WeChat
WeChat is a Chinese multi-purpose mobile app developed by Tencent that combines messaging, social media, and payment services into a single platform.
-
D.
BBM
BBM was a short-lived 1990s British rock supergroup featuring Jack Bruce, Ginger Baker, and Gary Moore.
-
E.
VK
VK is a major Russian technology company best known for operating the VKontakte social networking service and other popular online platforms.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91102c3c81908f571a1fff3bdd47 |
completed | April 14, 2026, 7:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bcd2a908190ad7d5ebf11b41551 |
completed | May 8, 2026, 3:43 a.m. |
Created at: April 10, 2026, 1:18 a.m.