Triple
T30575950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christian Slater as Daniel Molloy |
E778244
|
entity |
| Predicate | settingOfInterview |
P1957
|
FINISHED |
| Object | San Francisco |
—
|
NE NERFINISHED |
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: San Francisco | Statement: [Christian Slater as Daniel Molloy, settingOfInterview, San Francisco]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingOfInterview Context triple: [Christian Slater as Daniel Molloy, settingOfInterview, San Francisco]
-
A.
settingOfInvestigation
Indicates the context, environment, or circumstances in which an investigation takes place.
-
B.
setting
chosen
Indicates the place, time, or context in which an event, action, or interaction occurs.
-
C.
interviewTopic
Indicates that a particular subject or theme is the focus of discussion during an interview.
-
D.
settingOfDebate
Indicates the context, environment, or circumstances in which a particular debate takes place.
-
E.
settingOfKeyDecision
Indicates the place, context, or circumstances in which a significant decision is made.
- 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_69f2249f8c148190ae7eb3912cde112a |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f757898fe48190b124dc7301672623 |
completed | May 3, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69f754c484348190948d2a04ff228fb1 |
completed | May 3, 2026, 1:59 p.m. |
Created at: April 29, 2026, 8:22 p.m.