Triple
T17026072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | My Lucky Stars |
E413065
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Sibelle Hu
Sibelle Hu is a Taiwanese actress best known for her roles in 1980s Hong Kong action and comedy films.
|
E1248827
|
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: Sibelle Hu | Statement: [My Lucky Stars, hasCastMember, Sibelle Hu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sibelle Hu Context triple: [My Lucky Stars, hasCastMember, Sibelle Hu]
-
A.
Annette Lu
Annette Lu is a prominent Taiwanese politician and former vice president known for her leading role in Taiwan’s pro-democracy and feminist movements.
-
B.
Françoise Yip
Françoise Yip is a Canadian actress best known for her role alongside Jackie Chan in the action film "Rumble in the Bronx."
-
C.
Felicia Hano
Felicia Hano is an American artistic gymnast and former elite competitor who became a standout collegiate gymnast for the UCLA Bruins.
-
D.
Anna Mouglalis
Anna Mouglalis is a French actress and former Chanel muse known for her intense screen presence and roles in European art-house and crime films.
-
E.
Suzanne Buirgy
Suzanne Buirgy is a film producer known for her work on major animated features such as "Home" and other DreamWorks Animation projects.
- 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: Sibelle Hu Triple: [My Lucky Stars, hasCastMember, Sibelle Hu]
Generated description
Sibelle Hu is a Taiwanese actress best known for her roles in 1980s Hong Kong action and comedy films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sibelle Hu Target entity description: Sibelle Hu is a Taiwanese actress best known for her roles in 1980s Hong Kong action and comedy films.
-
A.
Annette Lu
Annette Lu is a prominent Taiwanese politician and former vice president known for her leading role in Taiwan’s pro-democracy and feminist movements.
-
B.
Françoise Yip
Françoise Yip is a Canadian actress best known for her role alongside Jackie Chan in the action film "Rumble in the Bronx."
-
C.
Felicia Hano
Felicia Hano is an American artistic gymnast and former elite competitor who became a standout collegiate gymnast for the UCLA Bruins.
-
D.
Anna Mouglalis
Anna Mouglalis is a French actress and former Chanel muse known for her intense screen presence and roles in European art-house and crime films.
-
E.
Suzanne Buirgy
Suzanne Buirgy is a film producer known for her work on major animated features such as "Home" and other DreamWorks Animation projects.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d5d46a5081908bc5681621dd8534 |
completed | April 18, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a012ed2ad708190a250762997611569 |
completed | May 11, 2026, 1:20 a.m. |
| NEDg | Description generation | batch_6a012f3285c481909b3de139bd7aee8b |
completed | May 11, 2026, 1:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a012fcf7dc08190af1851b56cf1667a |
completed | May 11, 2026, 1:24 a.m. |
Created at: April 10, 2026, 5:33 a.m.