Triple
T6128
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Rylands University Library |
E121
|
entity |
| Predicate | hasUserService |
P1077
|
FINISHED |
| Object | reading rooms |
—
|
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: reading rooms | Statement: [John Rylands University Library, hasUserService, reading rooms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUserService Context triple: [John Rylands University Library, hasUserService, reading rooms]
-
A.
hasRole
Indicates that an entity occupies, performs, or is assigned a specific role or function in relation to another entity or context.
-
B.
hasOnlinePlatform
Indicates that an entity operates, maintains, or is associated with a presence or service on an online platform.
-
C.
hasCollection
Indicates that an entity possesses, maintains, or is associated with a set or group of related items treated as a collection.
-
D.
hasPower
Indicates that one entity possesses authority, control, or influence over another entity or over a particular domain or resource.
-
E.
hasEmergencyServices
Indicates that the subject provides or is equipped with emergency response services (such as police, fire, or medical assistance).
- F. None of above. chosen
Provenance (4 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_69a23bb612708190b09f25385e4b63d1 |
completed | Feb. 28, 2026, 12:49 a.m. |
| NER | Named-entity recognition | batch_69a2421836f08190b54fc40edeb1a96b |
completed | Feb. 28, 2026, 1:17 a.m. |
| PD | Predicate disambiguation | batch_69a23fe064c881909496fd0e6b0e18d7 |
completed | Feb. 28, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69a242174cd4819095f78b221e1b41f1 |
completed | Feb. 28, 2026, 1:17 a.m. |
Created at: Feb. 28, 2026, 12:54 a.m.