Triple
T1594014
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Komaba Library |
E34237
|
entity |
| Predicate | hasUserType |
P121
|
FINISHED |
| Object | University of Tokyo students |
—
|
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: University of Tokyo students | Statement: [Komaba Library, hasUserType, University of Tokyo students]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUserType Context triple: [Komaba Library, hasUserType, University of Tokyo students]
-
A.
hasUser
Indicates that an entity is associated with or linked to a specific user.
-
B.
hasMemberType
chosen
Indicates that an entity includes or is associated with members belonging to a specified type or category.
-
C.
haveType
Indicates that an entity belongs to or is classified under a specified type or category.
-
D.
hasUserService
Indicates that an entity is associated with or utilizes a particular user-related service.
-
E.
hasVisitorType
Indicates the type or category of visitor associated with an entity (e.g., guest, customer, tourist, patient).
- 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_69a885fdcb9c819081ce6f0b8cd477dd |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a916d413f08190a4e137e5ed262e25 |
completed | March 5, 2026, 5:38 a.m. |
| PD | Predicate disambiguation | batch_69a907bfb39c8190a31e0be14d3d52e6 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:27 p.m.