Triple
T9342366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chang Chen |
E224792
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Chang Chen |
E224792
|
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: Chang Chen | Statement: [Chang Chen, name, Chang Chen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chang Chen Context triple: [Chang Chen, name, Chang Chen]
-
A.
Chang Chen
chosen
Chang Chen is a Taiwanese actor known for his roles in acclaimed films such as "Crouching Tiger, Hidden Dragon," "Happy Together," and various international productions.
-
B.
Haifan Lin
Haifan Lin is a prominent stem cell and developmental biologist known for his pioneering research on germline stem cells and RNA regulation.
-
C.
Lu Jun
Lu Jun is a family member of Lu Lingzi, the Chinese graduate student who was killed in the 2013 Boston Marathon bombing.
-
D.
Daniel Wu
Daniel Wu is a Hong Kong-American actor, director, and producer known for his roles in action and fantasy films as well as the TV series "Into the Badlands."
-
E.
Wu Jing
Wu Jing is a Chinese actor, director, and martial artist best known internationally for directing and starring in the blockbuster action films "Wolf Warrior" and "Wolf Warrior 2."
- 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_69ca842993248190a79ab06968994b86 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd4bb11cd08190b159a6066dd0ebf1 |
completed | April 1, 2026, 4:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0e4043e288190b05511d537b1614d |
completed | April 4, 2026, 10:12 a.m. |
Created at: March 30, 2026, 7:40 p.m.