Triple
T19839726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fu |
E476693
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Fu Xiaotian
Fu Xiaotian is a Chinese television journalist and talk show host known for her in-depth political interviews and international affairs coverage.
|
E1430556
|
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: Fu Xiaotian | Statement: [Fu, hasNotableBearer, Fu Xiaotian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fu Xiaotian Context triple: [Fu, hasNotableBearer, Fu Xiaotian]
-
A.
Gao Xiaoheng
Gao Xiaoheng was a member of the House of Gao, a noble family associated with imperial rule in Chinese history.
-
B.
Ma Xiaotian
Ma Xiaotian is a Chinese air force general who served as Commander of the People's Liberation Army Air Force.
-
C.
Jiang Xiaoyu
Jiang Xiaoyu is a Chinese sports official and event organizer best known for helping oversee and produce major ceremonies for the 2008 Beijing Olympic Games.
-
D.
Yang Tianyi
Yang Tianyi is an online content creator known for gaining popularity and a significant following on digital platforms.
-
E.
Jun Xia
Jun Xia is a Chinese architect best known for serving as the lead designer of Shanghai Tower, one of the world’s tallest skyscrapers.
- 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: Fu Xiaotian Triple: [Fu, hasNotableBearer, Fu Xiaotian]
Generated description
Fu Xiaotian is a Chinese television journalist and talk show host known for her in-depth political interviews and international affairs coverage.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fu Xiaotian Target entity description: Fu Xiaotian is a Chinese television journalist and talk show host known for her in-depth political interviews and international affairs coverage.
-
A.
Gao Xiaoheng
Gao Xiaoheng was a member of the House of Gao, a noble family associated with imperial rule in Chinese history.
-
B.
Ma Xiaotian
Ma Xiaotian is a Chinese air force general who served as Commander of the People's Liberation Army Air Force.
-
C.
Jiang Xiaoyu
Jiang Xiaoyu is a Chinese sports official and event organizer best known for helping oversee and produce major ceremonies for the 2008 Beijing Olympic Games.
-
D.
Yang Tianyi
Yang Tianyi is an online content creator known for gaining popularity and a significant following on digital platforms.
-
E.
Jun Xia
Jun Xia is a Chinese architect best known for serving as the lead designer of Shanghai Tower, one of the world’s tallest skyscrapers.
- 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65804be608190b49e110c3bf381bc |
completed | April 20, 2026, 4:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0883ddf09c81909415ac9e093a821d |
completed | May 16, 2026, 2:49 p.m. |
| NEDg | Description generation | batch_6a08849e605881909f3744eabf3c45ca |
completed | May 16, 2026, 2:52 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a088511c07c81908492b7fb54609cbc |
completed | May 16, 2026, 2:54 p.m. |
Created at: April 10, 2026, 1:50 p.m.