Triple
T15711807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Destiny |
E380856
|
entity |
| Predicate | wielder |
P706
|
FINISHED |
| Object |
Jen Yu
Jen Yu is a rebellious and highly skilled young noblewoman-turned-warrior from the film "Crouching Tiger, Hidden Dragon," whose secret mastery of martial arts drives much of the story’s conflict.
|
E1174049
|
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: Jen Yu | Statement: [Green Destiny, wielder, Jen Yu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jen Yu Context triple: [Green Destiny, wielder, Jen Yu]
-
A.
Jean Liu
Jean Liu is a prominent Chinese business executive and technology entrepreneur best known for her leadership role in ride-hailing giant Didi Chuxing.
-
B.
Candice Yu
Candice Yu is a Hong Kong actress known for her work in 1970s and 1980s Cantonese cinema and television.
-
C.
Jenny Chang
Jenny Chang is a Taiwanese entrepreneur best known as one of the co-founders of the global cybersecurity company Trend Micro.
-
D.
Jennifer Lien
Jennifer Lien is an American actress best known for her role as Kes on the television series "Star Trek: Voyager."
-
E.
Stella Yu
Stella Yu is a computer vision and machine learning researcher known for her work in perceptual organization, image segmentation, and computational models of visual perception.
- 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: Jen Yu Triple: [Green Destiny, wielder, Jen Yu]
Generated description
Jen Yu is a rebellious and highly skilled young noblewoman-turned-warrior from the film "Crouching Tiger, Hidden Dragon," whose secret mastery of martial arts drives much of the story’s conflict.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jen Yu Target entity description: Jen Yu is a rebellious and highly skilled young noblewoman-turned-warrior from the film "Crouching Tiger, Hidden Dragon," whose secret mastery of martial arts drives much of the story’s conflict.
-
A.
Jean Liu
Jean Liu is a prominent Chinese business executive and technology entrepreneur best known for her leadership role in ride-hailing giant Didi Chuxing.
-
B.
Candice Yu
Candice Yu is a Hong Kong actress known for her work in 1970s and 1980s Cantonese cinema and television.
-
C.
Jenny Chang
Jenny Chang is a Taiwanese entrepreneur best known as one of the co-founders of the global cybersecurity company Trend Micro.
-
D.
Jennifer Lien
Jennifer Lien is an American actress best known for her role as Kes on the television series "Star Trek: Voyager."
-
E.
Stella Yu
Stella Yu is a computer vision and machine learning researcher known for her work in perceptual organization, image segmentation, and computational models of visual perception.
- 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_69d86d9bf930819082b30cf6d169297c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04f8f5d6081908243fa59b46b7c76 |
completed | April 16, 2026, 2:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff82f22fc88190820ecb171041136d |
completed | May 9, 2026, 6:54 p.m. |
| NEDg | Description generation | batch_69ff83ff8c7481909fbc502143c1852f |
completed | May 9, 2026, 6:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff846436e48190b711da134c9a3b81 |
completed | May 9, 2026, 7 p.m. |
Created at: April 10, 2026, 4:45 a.m.