Triple
T1650430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guangzhou Metro |
E35677
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Kecun Station
Kecun Station is a major interchange stop on the Guangzhou Metro system in Guangzhou, China.
|
E514717
|
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: Kecun Station | Statement: [Guangzhou Metro, hasStation, Kecun Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kecun Station Context triple: [Guangzhou Metro, hasStation, Kecun Station]
-
A.
Senkawa Station
Senkawa Station is a subway station in Tokyo, Japan, serving passengers on the Tokyo Metro network.
-
B.
Naha Station
Naha Station is a major railway terminal in Naha, Okinawa, serving as a key transportation hub for the city and surrounding region.
-
C.
Kita-Yono Station
Kita-Yono Station is a railway station in Saitama, Japan, operated by JR East on the Saikyo Line and serving commuters in the surrounding urban area.
-
D.
Rokkomichi Station
Rokkomichi Station is a railway station in Kobe, Japan, serving as a key access point for visitors traveling to the Mount Rokko area.
-
E.
Heiwadai Station
Heiwadai Station is a subway station in Tokyo, Japan, served by the Tokyo Metro Fukutoshin Line (and typically also the Yurakucho Line), providing local commuter access in the Nerima ward.
- 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: Kecun Station Triple: [Guangzhou Metro, hasStation, Kecun Station]
Generated description
Kecun Station is a major interchange stop on the Guangzhou Metro system in Guangzhou, China.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kecun Station Target entity description: Kecun Station is a major interchange stop on the Guangzhou Metro system in Guangzhou, China.
-
A.
Senkawa Station
Senkawa Station is a subway station in Tokyo, Japan, serving passengers on the Tokyo Metro network.
-
B.
Naha Station
Naha Station is a major railway terminal in Naha, Okinawa, serving as a key transportation hub for the city and surrounding region.
-
C.
Kita-Yono Station
Kita-Yono Station is a railway station in Saitama, Japan, operated by JR East on the Saikyo Line and serving commuters in the surrounding urban area.
-
D.
Rokkomichi Station
Rokkomichi Station is a railway station in Kobe, Japan, serving as a key access point for visitors traveling to the Mount Rokko area.
-
E.
Heiwadai Station
Heiwadai Station is a subway station in Tokyo, Japan, served by the Tokyo Metro Fukutoshin Line (and typically also the Yurakucho Line), providing local commuter access in the Nerima ward.
- 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_69a8860568888190a32cd9f70acbba42 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90a66b58c819082d38ef1c805cf44 |
completed | March 5, 2026, 4:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf28e228608190a531da6024803e1b |
completed | March 21, 2026, 11:25 p.m. |
| NEDg | Description generation | batch_69bf29643ec88190b849eca03e6480c8 |
completed | March 21, 2026, 11:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf29bb3a9c8190827773c7057ce55a |
completed | March 21, 2026, 11:28 p.m. |
Created at: March 4, 2026, 7:29 p.m.