Triple
T26835424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chloe Simon |
E675615
|
entity |
| Predicate | filmSequelContext |
P1961
|
FINISHED |
| Object | sequel to 101 Dalmatians (1996 film) |
—
|
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: sequel to 101 Dalmatians (1996 film) | Statement: [Chloe Simon, filmSequelContext, sequel to 101 Dalmatians (1996 film)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmSequelContext Context triple: [Chloe Simon, filmSequelContext, sequel to 101 Dalmatians (1996 film)]
-
A.
continuesInSequels
Indicates that an element (such as a character, storyline, or theme) persists and appears again in one or more subsequent works in a series.
-
B.
hasSequelDepiction
Indicates that one depiction of something is followed by another depiction that continues its story or sequence.
-
C.
hasSequelAdaptation
Indicates that an original work has a subsequent adaptation that continues its story or follows it in sequence.
-
D.
hasSequel
chosen
Indicates that one work is followed by another work that continues its story, timeline, or thematic development.
-
E.
hasSecondSequel
Indicates that an entity has a second sequel, i.e., a third work in a series that continues its storyline or content.
- 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_69eee9b776448190993a60b67fcc9545 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f61adfa2c48190b9ac02679c1d0e5d |
completed | May 2, 2026, 3:40 p.m. |
| PD | Predicate disambiguation | batch_69f611ad2eb48190ac1ed0090f13f7a9 |
completed | May 2, 2026, 3:01 p.m. |
Created at: April 27, 2026, 5:04 a.m.