Triple
T38676569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roy Williams Jr. |
E943757
|
entity |
| Predicate | hasSettingOfShow |
P3538
|
FINISHED |
| Object | bar in Pittsburgh, Pennsylvania |
—
|
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: bar in Pittsburgh, Pennsylvania | Statement: [Roy Williams Jr., hasSettingOfShow, bar in Pittsburgh, Pennsylvania]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSettingOfShow Context triple: [Roy Williams Jr., hasSettingOfShow, bar in Pittsburgh, Pennsylvania]
-
A.
hasSetting
chosen
Indicates that an entity takes place, occurs, or exists within a particular environment, context, or location.
-
B.
hasSettingBy
Indicates that something (such as a work, event, or scenario) has its contextual environment, location, or background defined or established by a particular agent or source.
-
C.
hasSettingDetail
Indicates that an entity is associated with a specific contextual or environmental detail that characterizes its setting.
-
D.
hasSettingRole
Indicates that an entity participates in a setting by fulfilling a specific contextual or functional role within it.
-
E.
hasShowUse
Indicates that something is used or presented as an example or demonstration in a show, display, or illustrative context.
- 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_69f76eec28708190b9c82a505fc278e0 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a00dc330b148190aaae2ac6a5327960 |
completed | May 10, 2026, 7:27 p.m. |
| PD | Predicate disambiguation | batch_6a00d9d2904881909dafbfe7b9e5ad81 |
completed | May 10, 2026, 7:17 p.m. |
Created at: May 3, 2026, 4:33 p.m.