Triple
T3955059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huizhou (historical region) |
E84955
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Qimen
Qimen is a historic county-level town in Anhui Province, China, best known as the origin of the famous Qimen (Keemun) black tea.
|
E402897
|
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: Qimen | Statement: [Huizhou (historical region), hasCity, Qimen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Qimen Context triple: [Huizhou (historical region), hasCity, Qimen]
-
A.
Andingmen
Andingmen is a historic area and former city gate site in central Beijing, known for its traditional neighborhoods and proximity to key cultural landmarks.
-
B.
Dongmen
Dongmen is a key Taipei Metro station in central Taipei that serves as a busy transfer point between multiple subway lines and nearby commercial and residential areas.
-
C.
Shuaiba
Shuaiba is an industrial and port town in Kuwait known for its major petrochemical and shipping facilities along the Persian Gulf.
-
D.
Chardzhou
Chardzhou is the former name of Turkmenabat, a major city in eastern Turkmenistan located on the Amu Darya River.
-
E.
Guguan
Guguan is an uninhabited volcanic island in the Northern Mariana Islands chain in the western Pacific Ocean.
- 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: Qimen Triple: [Huizhou (historical region), hasCity, Qimen]
Generated description
Qimen is a historic county-level town in Anhui Province, China, best known as the origin of the famous Qimen (Keemun) black tea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Qimen Target entity description: Qimen is a historic county-level town in Anhui Province, China, best known as the origin of the famous Qimen (Keemun) black tea.
-
A.
Andingmen
Andingmen is a historic area and former city gate site in central Beijing, known for its traditional neighborhoods and proximity to key cultural landmarks.
-
B.
Dongmen
Dongmen is a key Taipei Metro station in central Taipei that serves as a busy transfer point between multiple subway lines and nearby commercial and residential areas.
-
C.
Shuaiba
Shuaiba is an industrial and port town in Kuwait known for its major petrochemical and shipping facilities along the Persian Gulf.
-
D.
Chardzhou
Chardzhou is the former name of Turkmenabat, a major city in eastern Turkmenistan located on the Amu Darya River.
-
E.
Guguan
Guguan is an uninhabited volcanic island in the Northern Mariana Islands chain in the western Pacific Ocean.
- 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_69aed934fbfc8190847068e4546de963 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef93d742c81908639c843193d78fd |
completed | March 9, 2026, 4:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b533aea08c8190b83d83e3ba89848c |
completed | March 14, 2026, 10:08 a.m. |
| NEDg | Description generation | batch_69b537f7e2e481909b7a337c130bca7a |
completed | March 14, 2026, 10:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b538a7f8e4819087a74e96255e7c45 |
completed | March 14, 2026, 10:30 a.m. |
Created at: March 9, 2026, 3:30 p.m.