Triple
T28837033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Janet Leach |
E728210
|
entity |
| Predicate | basedOnRealEventsDepictedIn |
P53505
|
FINISHED |
| Object | "Appropriate Adult" |
—
|
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: "Appropriate Adult" | Statement: [Janet Leach, basedOnRealEventsDepictedIn, "Appropriate Adult"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnRealEventsDepictedIn Context triple: [Janet Leach, basedOnRealEventsDepictedIn, "Appropriate Adult"]
-
A.
basedOnRealLife
Indicates that something is derived from, inspired by, or directly adapted from actual real-world events, people, or situations.
-
B.
basedOnEventsDescribedIn
Indicates that something is derived from, inspired by, or constructed using the events described in another source.
-
C.
usesRealHistoricalEvents
chosen
Indicates that the subject incorporates or is based on actual events that occurred in real history.
-
D.
depictionBasedOn
Indicates that one depiction is created using another work, image, or representation as its source or reference.
-
E.
capturedInFictionalEvent
Indicates that an entity is depicted as being captured during a fictional event or scenario.
- 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_69f0319dc6088190bbfaa206d40ed74a |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69fcdf2394748190b35cead3e208447d |
completed | May 7, 2026, 6:51 p.m. |
| PD | Predicate disambiguation | batch_69fcdbe344ec8190a0471911952f4b82 |
completed | May 7, 2026, 6:37 p.m. |
Created at: April 28, 2026, 6:39 a.m.