Triple
T6314012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Utsunomiya Station |
E141570
|
entity |
| Predicate | hasStationNumber |
P1289
|
FINISHED |
| Object |
UTS
UTS is the station code used to identify Utsunomiya Station in Japan’s railway system.
|
E583845
|
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: UTS | Statement: [Utsunomiya Station, hasStationNumber, UTS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UTS Context triple: [Utsunomiya Station, hasStationNumber, UTS]
-
A.
UTS
UTS is a major Australian public research university in Sydney known for its industry-focused education and modern urban campus.
-
B.
UTS
UTS is a highly selective independent secondary school affiliated with the University of Toronto, known for its strong academic programs and gifted education.
-
C.
UTS #35
UTS #35 is a Unicode Technical Standard that defines the Locale Data Markup Language (LDML) used for internationalization data in the Unicode CLDR project.
-
D.
UTS Central
UTS Central is a major contemporary teaching, learning, and student services hub on the University of Technology Sydney campus, known for its distinctive glass design and integration of library and collaborative spaces.
-
E.
UTS #10
UTS #10 is the Unicode Collation Algorithm standard that defines how to consistently compare and sort Unicode text across different languages and platforms.
- 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: UTS Triple: [Utsunomiya Station, hasStationNumber, UTS]
Generated description
UTS is the station code used to identify Utsunomiya Station in Japan’s railway system.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: UTS Target entity description: UTS is the station code used to identify Utsunomiya Station in Japan’s railway system.
-
A.
UTS
UTS is a major Australian public research university in Sydney known for its industry-focused education and modern urban campus.
-
B.
UTS
UTS is a highly selective independent secondary school affiliated with the University of Toronto, known for its strong academic programs and gifted education.
-
C.
UTS #35
UTS #35 is a Unicode Technical Standard that defines the Locale Data Markup Language (LDML) used for internationalization data in the Unicode CLDR project.
-
D.
UTS Central
UTS Central is a major contemporary teaching, learning, and student services hub on the University of Technology Sydney campus, known for its distinctive glass design and integration of library and collaborative spaces.
-
E.
UTS #10
UTS #10 is the Unicode Collation Algorithm standard that defines how to consistently compare and sort Unicode text across different languages and platforms.
- 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_69c008d00efc8190a36c05b4b4a3bf4b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c064a075dc8190acf7ec010cb4b00c |
completed | March 22, 2026, 9:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5e46feeec8190bdb39a48c92bacf1 |
completed | March 27, 2026, 1:59 a.m. |
| NEDg | Description generation | batch_69c5ee04b2388190b36dc9bb23b4a18a |
completed | March 27, 2026, 2:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c5ee780bb08190a97c90804f9c6b68 |
completed | March 27, 2026, 2:42 a.m. |
Created at: March 22, 2026, 4:28 p.m.