Triple
T16106059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Farewell |
E390740
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Lu Hong
Lu Hong is an actor known for appearing in the acclaimed family drama film "The Farewell."
|
E1212713
|
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: Lu Hong | Statement: [The Farewell, starring, Lu Hong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lu Hong Context triple: [The Farewell, starring, Lu Hong]
-
A.
Li Hong
Li Hong was a Tang dynasty crown prince and the eldest son of Empress Wu Zetian and Emperor Gaozong, known for his reputation as a benevolent and capable heir before his early death.
-
B.
Wu Hong
Wu Hong is a Chinese art historian and curator renowned for his scholarship on traditional and contemporary Chinese art.
-
C.
Ma Hongkui
Ma Hongkui was a prominent Chinese Muslim warlord and Kuomintang general who controlled Ningxia during the Republic of China era.
-
D.
Huang Shaohong
Huang Shaohong was a prominent Chinese Nationalist military and political leader who played a key role in the development and governance of Guangxi during the Republican era.
-
E.
Li Wen
Li Wen, better known by his temple name Emperor Yizong of Tang, was a 9th-century emperor of the Tang dynasty in 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: Lu Hong Triple: [The Farewell, starring, Lu Hong]
Generated description
Lu Hong is an actor known for appearing in the acclaimed family drama film "The Farewell."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lu Hong Target entity description: Lu Hong is an actor known for appearing in the acclaimed family drama film "The Farewell."
-
A.
Li Hong
Li Hong was a Tang dynasty crown prince and the eldest son of Empress Wu Zetian and Emperor Gaozong, known for his reputation as a benevolent and capable heir before his early death.
-
B.
Wu Hong
Wu Hong is a Chinese art historian and curator renowned for his scholarship on traditional and contemporary Chinese art.
-
C.
Ma Hongkui
Ma Hongkui was a prominent Chinese Muslim warlord and Kuomintang general who controlled Ningxia during the Republic of China era.
-
D.
Huang Shaohong
Huang Shaohong was a prominent Chinese Nationalist military and political leader who played a key role in the development and governance of Guangxi during the Republican era.
-
E.
Li Wen
Li Wen, better known by his temple name Emperor Yizong of Tang, was a 9th-century emperor of the Tang dynasty in 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_69d87f1a8dd881909f1de6ef78849874 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e1ff6d81d081909e1315f4dbfd7369 |
completed | April 17, 2026, 9:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00456d5e74819080c838468ec015ef |
completed | May 10, 2026, 8:44 a.m. |
| NEDg | Description generation | batch_6a0046dd6f988190a36e6660ff7dfa19 |
completed | May 10, 2026, 8:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00474080b0819084aa13a4b30fa1dd |
completed | May 10, 2026, 8:52 a.m. |
Created at: April 10, 2026, 5 a.m.