Triple
T30974055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Smith's |
E789178
|
entity |
| Predicate | targetConsumerContext |
P97560
|
FINISHED |
| Object | pubs |
—
|
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: pubs | Statement: [John Smith's, targetConsumerContext, pubs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetConsumerContext Context triple: [John Smith's, targetConsumerContext, pubs]
-
A.
targetConsumerInStory
Indicates that a specified consumer is the intended or focal target within the context of a particular story or narrative.
-
B.
intendedTargetContext
Indicates the context, situation, or setting that an action, message, or object is specifically designed or meant to be used in or directed toward.
-
C.
targetConsumerNeed
Indicates that something is intended to address, satisfy, or be directed toward a specific need or requirement of a consumer.
-
D.
supplyContext
Indicates that one entity provides relevant background information or situational details that clarify or frame another entity, action, or statement.
-
E.
primaryConsumptionContext
chosen
Indicates the main situation, setting, or context in which something is typically used, consumed, or experienced.
- 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_69f224c4831c8190be53924ec25a150a |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fd68abf52881909c5a390c362b7c59 |
completed | May 8, 2026, 4:38 a.m. |
| PD | Predicate disambiguation | batch_69fd6812d0c88190930d8fa2d4b92490 |
completed | May 8, 2026, 4:35 a.m. |
Created at: April 29, 2026, 8:55 p.m.