Triple
T10677956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tomb Raider (2018 film) |
E251666
|
entity |
| Predicate | portrays |
P264
|
FINISHED |
| Object |
Daniel Wu as Lu Ren
Daniel Wu as Lu Ren is a rugged ship captain and ally who helps Lara Croft on her perilous expedition in the 2018 film "Tomb Raider."
|
E878751
|
NE FINISHED |
How this triple was built (4 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: Daniel Wu as Lu Ren | Statement: [Tomb Raider (2018 film), portrays, Daniel Wu as Lu Ren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Wu as Lu Ren Context triple: [Tomb Raider (2018 film), portrays, Daniel Wu as Lu Ren]
-
A.
Lin Sen
Lin Sen was a Chinese politician who served as the chairman of the National Government of the Republic of China during the turbulent years leading up to and including much of the Second Sino-Japanese War.
-
B.
Cheng Tai Shen
Cheng Tai Shen is an actor known for his role in the critically acclaimed Mexican drama film "Biutiful."
-
C.
Tie Luo Han
Tie Luo Han is a famous and highly prized Wuyi rock oolong tea from China, known for its rich, roasted flavor and mineral complexity.
-
D.
Thiệu Long
Thiệu Long was an imperial era name used during the Vietnamese Trần dynasty to designate a specific reign period.
-
E.
Fan Ji
Fan Ji was a consort of King Zhuang of Chu, a prominent ruler of the Spring and Autumn period in ancient China.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Daniel Wu as Lu Ren Triple: [Tomb Raider (2018 film), portrays, Daniel Wu as Lu Ren]
Generated description
Daniel Wu as Lu Ren is a rugged ship captain and ally who helps Lara Croft on her perilous expedition in the 2018 film "Tomb Raider."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daniel Wu as Lu Ren Target entity description: Daniel Wu as Lu Ren is a rugged ship captain and ally who helps Lara Croft on her perilous expedition in the 2018 film "Tomb Raider."
-
A.
Lin Sen
Lin Sen was a Chinese politician who served as the chairman of the National Government of the Republic of China during the turbulent years leading up to and including much of the Second Sino-Japanese War.
-
B.
Cheng Tai Shen
Cheng Tai Shen is an actor known for his role in the critically acclaimed Mexican drama film "Biutiful."
-
C.
Tie Luo Han
Tie Luo Han is a famous and highly prized Wuyi rock oolong tea from China, known for its rich, roasted flavor and mineral complexity.
-
D.
Thiệu Long
Thiệu Long was an imperial era name used during the Vietnamese Trần dynasty to designate a specific reign period.
-
E.
Fan Ji
Fan Ji was a consort of King Zhuang of Chu, a prominent ruler of the Spring and Autumn period in ancient China.
- F. None of above. chosen
Provenance (5 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_69d6aa5bd7c08190a816e733b4045c23 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fb9684e48190b2786823723cde6c |
completed | April 9, 2026, 1:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9887e974c81908c4943339ea9a93f |
completed | April 10, 2026, 11:32 p.m. |
| NEDg | Description generation | batch_69d98aea391c81909ec64a29053c35c1 |
completed | April 10, 2026, 11:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d98c013348819094bde38a057257b4 |
completed | April 10, 2026, 11:47 p.m. |
Created at: April 8, 2026, 9:10 p.m.