Triple
T17435838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Debbie Edwards |
E423998
|
entity |
| Predicate | characterAgeLaterInFilm |
P94896
|
FINISHED |
| Object | teenager |
—
|
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: teenager | Statement: [Debbie Edwards, characterAgeLaterInFilm, teenager]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterAgeLaterInFilm Context triple: [Debbie Edwards, characterAgeLaterInFilm, teenager]
-
A.
protagonistAgeRelativeToPrequel
Indicates how the protagonist’s age in the current work compares to their age in a preceding prequel story.
-
B.
ageInFirstFilm
Indicates the age a person was when they appeared in their first film.
-
C.
portrayedByCharacterAgeApprox
Indicates that an entity is portrayed by a character whose age is approximately a specified value or age range.
-
D.
characterAgeDescriptor
chosen
Indicates how a character’s age is qualitatively described or categorized (e.g., young, middle-aged, elderly) rather than given as a specific number.
-
E.
portraysFromAge
Indicates that one entity depicts another entity starting from a specified age of the depicted entity.
- 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_69d889d88b6081908bada047f5b3ba51 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4490426008190b474ed76aca5d6f3 |
completed | April 19, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69e3b030eac481909b8402719cc3102e |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:46 a.m.