Triple
T17069893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mulan (2020 film) |
E414187
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Xana Tang
Xana Tang is a New Zealand actress known for her role in Disney's live-action adaptation of "Mulan" (2020) and for her work in film and television across Australasia.
|
E1247423
|
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: Xana Tang | Statement: [Mulan (2020 film), castMember, Xana Tang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xana Tang Context triple: [Mulan (2020 film), castMember, Xana Tang]
-
A.
Deg Xinag
Deg Xinag is an endangered Northern Athabaskan language traditionally spoken by the Deg Hit’an people of interior Alaska.
-
B.
Xandra
Xandra is a shortened, informal given name derived from Alexandra, often used as a modern, distinctive feminine name.
-
C.
Linzi
Linzi was the prominent ancient Chinese city that served as the political, economic, and cultural center of the powerful State of Qi during the Zhou dynasty.
-
D.
Xelaína
Xelaína is the Spanish demonym used to refer to a female inhabitant or native of Xelajú (Quetzaltenango) in Guatemala.
-
E.
Daji
Daji is a legendary figure in Chinese mythology, often depicted as a beautiful but malevolent consort whose influence is blamed for the downfall of the Shang dynasty.
- 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: Xana Tang Triple: [Mulan (2020 film), castMember, Xana Tang]
Generated description
Xana Tang is a New Zealand actress known for her role in Disney's live-action adaptation of "Mulan" (2020) and for her work in film and television across Australasia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Xana Tang Target entity description: Xana Tang is a New Zealand actress known for her role in Disney's live-action adaptation of "Mulan" (2020) and for her work in film and television across Australasia.
-
A.
Deg Xinag
Deg Xinag is an endangered Northern Athabaskan language traditionally spoken by the Deg Hit’an people of interior Alaska.
-
B.
Xandra
Xandra is a shortened, informal given name derived from Alexandra, often used as a modern, distinctive feminine name.
-
C.
Linzi
Linzi was the prominent ancient Chinese city that served as the political, economic, and cultural center of the powerful State of Qi during the Zhou dynasty.
-
D.
Xelaína
Xelaína is the Spanish demonym used to refer to a female inhabitant or native of Xelajú (Quetzaltenango) in Guatemala.
-
E.
Daji
Daji is a legendary figure in Chinese mythology, often depicted as a beautiful but malevolent consort whose influence is blamed for the downfall of the Shang dynasty.
- 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_69d886cef44c8190ba56c44b4e863e64 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbbfb1f08190807301ff6e573cf5 |
completed | April 18, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01235278d48190bacc7881b9eb1ea1 |
completed | May 11, 2026, 12:31 a.m. |
| NEDg | Description generation | batch_6a012425fb808190ad9bcada9429e437 |
completed | May 11, 2026, 12:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01248b53d88190a4ec4fa6cee89bb1 |
completed | May 11, 2026, 12:36 a.m. |
Created at: April 10, 2026, 5:34 a.m.