Triple
T17377687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abcam |
E422482
|
entity |
| Predicate | hasOfficeLocation |
P1268
|
FINISHED |
| Object | Shanghai, China |
—
|
NE NERFINISHED |
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: Shanghai, China | Statement: [Abcam, hasOfficeLocation, Shanghai, China]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shanghai, China Context triple: [Abcam, hasOfficeLocation, Shanghai, China]
-
A.
Shanghai
chosen
Shanghai is a major global financial hub and China’s largest city, known for its modern skyline, historic waterfront, and role as a center of international business and trade.
-
B.
Shanghai
Shanghai is an unincorporated community located in Berkeley County, West Virginia, United States.
-
C.
Shanghai
Shanghai is a major Ethereum network upgrade that introduced key changes such as enabling staked ETH withdrawals and improving the protocol’s efficiency and flexibility.
-
D.
Ningbo, China
Ningbo, China is a major port city in eastern Zhejiang province known for its long maritime history and role as a key hub in regional and international trade.
-
E.
Shenzhen, China
Shenzhen, China is a major southern Chinese metropolis known for its rapid transformation into a global technology and manufacturing hub bordering Hong Kong.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a6ea56c8190b56d966ccf2c91f7 |
completed | April 19, 2026, 2:14 a.m. |
Created at: April 10, 2026, 5:45 a.m.