Triple
T35190974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Slacker |
E1016117
|
entity |
| Predicate | depictsCityAsCharacter |
P193085
|
FINISHED |
| Object | Austin, Texas |
—
|
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: Austin, Texas | Statement: [Slacker, depictsCityAsCharacter, Austin, Texas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictsCityAsCharacter Context triple: [Slacker, depictsCityAsCharacter, Austin, Texas]
-
A.
protagonistCity
Indicates the city in which the main character or protagonist of a work is primarily based or associated.
-
B.
cityDepicted
Indicates that a work (such as an image, artwork, or document) visually or representationally depicts a particular city.
-
C.
modeledInCity
Indicates that an entity (such as a model or design) was created, developed, or constructed within a particular city.
-
D.
partOfFictionalCity
Indicates that one entity is a component, area, or subdivision within a larger fictional city.
-
E.
hasFictionalCityContext
Indicates that something is associated with, set in, or contextualized by a fictional city.
- 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_69f76ddd815c8190b822eea06630f9fb |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fd37b695c88190855801626f91c4cd |
completed | May 8, 2026, 1:09 a.m. |
| PD | Predicate disambiguation | batch_69fd374cccf08190a230e87164af5938 |
completed | May 8, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69fd37b45b4481908b947b52fbcb7ade |
completed | May 8, 2026, 1:09 a.m. |
Created at: May 3, 2026, 4:02 p.m.