Triple
T7288356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Insidious: Chapter 2 |
E163930
|
entity |
| Predicate | hasFranchiseInstallmentNumber |
P42681
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Insidious: Chapter 2, hasFranchiseInstallmentNumber, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFranchiseInstallmentNumber Context triple: [Insidious: Chapter 2, hasFranchiseInstallmentNumber, 2]
-
A.
franchiseInstallmentNumber
chosen
Indicates the sequential position or installment number of a work within a larger franchise or series.
-
B.
hasMainInstallment
Indicates that one entity is the primary or principal installment (e.g., main payment or main part of a series) associated with another entity.
-
C.
isFinalInstallmentOf
Indicates that one work or item is the concluding or last part in a series, sequence, or collection of related works.
-
D.
franchiseEntryNumber
Indicates the ordinal position or sequence number of an entry within a franchise series.
-
E.
hasFranchiseSlot
Indicates that an entity holds or is assigned a specific franchise position, license, or allocation within a larger franchising structure.
- 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_69c6886093b88190a254b1ce6db8bae7 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6eb6a73fc8190ae5ce81fd3e46d87 |
completed | March 27, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69c6e76c5fbc8190b378830082f11cb0 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 2:59 p.m.