Triple
T529542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lonely Room |
E10993
|
entity |
| Predicate | hasSettingContext |
P3538
|
FINISHED |
| Object | rural Oklahoma territory |
—
|
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: rural Oklahoma territory | Statement: [Lonely Room, hasSettingContext, rural Oklahoma territory]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSettingContext Context triple: [Lonely Room, hasSettingContext, rural Oklahoma territory]
-
A.
hasSetting
chosen
Indicates that an entity takes place, occurs, or exists within a particular environment, context, or location.
-
B.
hasLanguageContext
Indicates that an entity is associated with or interpreted within a specific language or linguistic context.
-
C.
hasConfiguration
Indicates that an entity is associated with or defined by a particular configuration or setup.
-
D.
hasDevolutionContext
Indicates that something is associated with, or occurs within, a specific context of devolution (such as the transfer or delegation of powers or responsibilities).
-
E.
hasScope
Indicates that one entity defines, limits, or encompasses the range, extent, or applicability within which another entity operates or is valid.
- 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_69a2e84b16c4819088d284c47c3a7968 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1d4984c8190ac372171b16bb5e4 |
completed | Feb. 28, 2026, 1:47 p.m. |
| PD | Predicate disambiguation | batch_69a2f01ac3ec8190a94a05955532c7fa |
completed | Feb. 28, 2026, 1:39 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.