Triple
T23017359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Talman |
E573066
|
entity |
| Predicate | PSATopic |
P150693
|
FINISHED |
| Object | dangers of smoking |
—
|
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: dangers of smoking | Statement: [William Talman, PSATopic, dangers of smoking]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: PSATopic Context triple: [William Talman, PSATopic, dangers of smoking]
-
A.
topicOfDialogue
Indicates that a particular subject or theme is the main focus of a dialogue or conversation between entities.
-
B.
questionTopic
Indicates that a question is about, concerns, or is primarily focused on a particular topic or subject.
-
C.
coveredTopics
Indicates that certain subjects or themes have been addressed or included within a discussion, document, or activity.
-
D.
interviewTopic
Indicates that a particular subject or theme is the focus of discussion during an interview.
-
E.
primaryTopicOf
Indicates that a given subject is the main or central topic described by another resource (such as a document, page, or record).
- 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_69e245b764cc8190a51be76f1d9611e1 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f183e59a1c8190b8048a399a4727cb |
completed | April 29, 2026, 4:07 a.m. |
| PD | Predicate disambiguation | batch_69ef3b9cd5488190bcd23183179f48cd |
completed | April 27, 2026, 10:34 a.m. |
| PDg | Predicate description generation | batch_69ef538b29c081908fa56ee35a1dcee7 |
completed | April 27, 2026, 12:16 p.m. |
Created at: April 17, 2026, 3:52 p.m.