Triple
T5988961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liezi |
E133296
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object |
Lie Yukou
Lie Yukou is an ancient Chinese philosopher traditionally credited with authoring the Daoist classic "Liezi," though his historical existence remains uncertain.
|
E561005
|
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: Lie Yukou | Statement: [Liezi, author, Lie Yukou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lie Yukou Context triple: [Liezi, author, Lie Yukou]
-
A.
Yuji
Yuji is a common Japanese masculine given name used by various real and fictional individuals.
-
B.
Kaoru
Kaoru is a central character in the later chapters of the classic Japanese novel "The Tale of Genji," known for his gentle nature and complex romantic entanglements.
-
C.
Iori
The Iori is a river in the South Caucasus that flows through eastern Georgia and parts of Azerbaijan before joining the Kura River.
-
D.
Saitō Makoto
Saitō Makoto was a Japanese admiral and statesman who served as Governor-General of Korea and later as Prime Minister of Japan during the early Shōwa period.
-
E.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
- 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: Lie Yukou Triple: [Liezi, author, Lie Yukou]
Generated description
Lie Yukou is an ancient Chinese philosopher traditionally credited with authoring the Daoist classic "Liezi," though his historical existence remains uncertain.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lie Yukou Target entity description: Lie Yukou is an ancient Chinese philosopher traditionally credited with authoring the Daoist classic "Liezi," though his historical existence remains uncertain.
-
A.
Yuji
Yuji is a common Japanese masculine given name used by various real and fictional individuals.
-
B.
Kaoru
Kaoru is a central character in the later chapters of the classic Japanese novel "The Tale of Genji," known for his gentle nature and complex romantic entanglements.
-
C.
Iori
The Iori is a river in the South Caucasus that flows through eastern Georgia and parts of Azerbaijan before joining the Kura River.
-
D.
Saitō Makoto
Saitō Makoto was a Japanese admiral and statesman who served as Governor-General of Korea and later as Prime Minister of Japan during the early Shōwa period.
-
E.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
- 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_69c0087010d081908bb8142342d63330 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04dc76fd481908cc3f327e532a1a6 |
completed | March 22, 2026, 8:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c10854969c8190b9be249f26ad2f47 |
completed | March 23, 2026, 9:31 a.m. |
| NEDg | Description generation | batch_69c109bf2fb4819091915b2e10b629b8 |
completed | March 23, 2026, 9:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c10a5e061c81909e8085f3210452dc |
completed | March 23, 2026, 9:39 a.m. |
Created at: March 22, 2026, 4:04 p.m.