Triple
T13920795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gunnersbury station |
E334735
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object |
ZGG
ZGG is the National Rail station code assigned to Gunnersbury railway station in London.
|
E1069645
|
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: ZGG | Statement: [Gunnersbury station, hasStationCode, ZGG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ZGG Context triple: [Gunnersbury station, hasStationCode, ZGG]
-
A.
ZGGG
ZGGG is the ICAO airport code for Guangzhou Baiyun International Airport, a major aviation hub serving Guangzhou in southern China.
-
B.
ZG
ZG is the vehicle registration code used on license plates for the city of Zagreb, the capital of Croatia.
-
C.
ZGB
ZGB is the abbreviation for the Swiss Civil Code, the fundamental body of private law governing civil matters in Switzerland.
-
D.
DZG
DZG is the vehicle registration code assigned to cars registered in the Bogatynia area of Poland.
-
E.
ZGHA
ZGHA is the ICAO airport code for Changsha Huanghua International Airport, a major air transport hub serving Changsha and the Hunan province in China.
- 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: ZGG Triple: [Gunnersbury station, hasStationCode, ZGG]
Generated description
ZGG is the National Rail station code assigned to Gunnersbury railway station in London.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ZGG Target entity description: ZGG is the National Rail station code assigned to Gunnersbury railway station in London.
-
A.
ZGGG
ZGGG is the ICAO airport code for Guangzhou Baiyun International Airport, a major aviation hub serving Guangzhou in southern China.
-
B.
ZG
ZG is the vehicle registration code used on license plates for the city of Zagreb, the capital of Croatia.
-
C.
ZGB
ZGB is the abbreviation for the Swiss Civil Code, the fundamental body of private law governing civil matters in Switzerland.
-
D.
DZG
DZG is the vehicle registration code assigned to cars registered in the Bogatynia area of Poland.
-
E.
ZGHA
ZGHA is the ICAO airport code for Changsha Huanghua International Airport, a major air transport hub serving Changsha and the Hunan province in China.
- 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_69d81c5f739081908bc05b2461f54828 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2aa428ac819084e7c4b244d15f20 |
completed | April 14, 2026, 11:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce7c4a788190a1e7619a00ab0c2e |
completed | May 3, 2026, 10:38 p.m. |
| NEDg | Description generation | batch_69f9fd5b82f48190b0b89ddca25883cc |
completed | May 5, 2026, 2:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f9fea0a9dc8190b5b65dfec9626949 |
completed | May 5, 2026, 2:28 p.m. |
Created at: April 9, 2026, 10:16 p.m.