Triple
T15419446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bay City (fictional) |
E369333
|
entity |
| Predicate | usedForStoryTheme |
P76865
|
FINISHED |
| Object | urban crime |
—
|
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: urban crime | Statement: [Bay City (fictional), usedForStoryTheme, urban crime]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedForStoryTheme Context triple: [Bay City (fictional), usedForStoryTheme, urban crime]
-
A.
hasThemeInStory
chosen
Indicates that a particular theme is present or plays a significant role within a given story.
-
B.
themeFor
Indicates that something serves as the central subject, topic, or focus for another thing (such as an event, work, or activity).
-
C.
theme
Indicates the entity that is the primary participant or content affected or characterized by an action, event, or state.
-
D.
fictionalTheme
Indicates that a work, element, or context is centered around or characterized by a fictional theme or motif.
-
E.
themedAs
Indicates that something is characterized, styled, or organized according to a particular theme or motif.
- 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_69d85a1849f48190bf898068b2806fae |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ebce4f48190ba282ecb4fb2f6fa |
completed | April 16, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69ded27f45548190a6d2b1b85cb47444 |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:20 a.m.