Triple
T2828195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyoto Municipal Subway |
E54972
|
entity |
| Predicate | hasRoleInCity |
P8234
|
FINISHED |
| Object | backbone of Kyoto public transport network |
—
|
LITERAL FINISHED |
How this triple was built (2 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: backbone of Kyoto public transport network | Statement: [Kyoto Municipal Subway, hasRoleInCity, backbone of Kyoto public transport network]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRoleInCity Context triple: [Kyoto Municipal Subway, hasRoleInCity, backbone of Kyoto public transport network]
-
A.
hasCityRole
chosen
Indicates that an entity holds or is assigned a specific role, function, or status within a particular city.
-
B.
isInCity
Indicates that one entity is located within the geographical boundaries of a specified city.
-
C.
hasUrbanRole
Indicates that an entity plays a specific functional or social role within an urban or city context.
-
D.
hasComponentCity
Indicates that an entity includes or is composed of one or more cities as its constituent parts.
-
E.
hasAssociatedCity
Indicates that one entity is linked or related to a specific city, typically as its location, base, or primary area of association.
- F. None of above.
Provenance (3 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_69ab49e100c0819082a40cb797383243 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde95d8148190bcae8b0659a5c116 |
completed | March 7, 2026, 8:15 a.m. |
| PD | Predicate disambiguation | batch_69abdd0acab881909e8c25cbef83678c |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:59 p.m.