Triple
T31007414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gail Dwyer |
E790106
|
entity |
| Predicate | hasOnScreenResidence |
P179512
|
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: [Gail Dwyer, hasOnScreenResidence, San Francisco]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOnScreenResidence Context triple: [Gail Dwyer, hasOnScreenResidence, San Francisco]
-
A.
hasOnScreenOccupation
Indicates that an entity is depicted as having a particular occupation or job within an on-screen context (e.g., in a film, TV show, or other visual media).
-
B.
hasOnScreenDynamic
Indicates that one entity displays or presents another entity as a changing or interactive element on a screen.
-
C.
hasResidenceTime
Indicates the duration for which something remains or stays within a particular place, system, or state.
-
D.
hasOnScreenRelative
Indicates that one entity has a family member who appears or is depicted on screen in relation to it.
-
E.
hasResidenceFeature
Indicates that a residence possesses or is characterized by a specific feature or attribute.
- 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_69f224c73ca48190a1e46cb58ad4045b |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f722c1bc648190a79bfdc722dcaaa4 |
completed | May 3, 2026, 10:26 a.m. |
| PD | Predicate disambiguation | batch_69f72153a9188190b02adc84e1be4af8 |
completed | May 3, 2026, 10:20 a.m. |
| PDg | Predicate description generation | batch_69f7221bc57c819085c1464a45e61b2f |
completed | May 3, 2026, 10:23 a.m. |
Created at: April 29, 2026, 8:57 p.m.