Triple
T5742463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Witch (Into the Woods) |
E126646
|
entity |
| Predicate | alignmentWithProtagonists |
P47185
|
FINISHED |
| Object | both helper and adversary |
—
|
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: both helper and adversary | Statement: [The Witch (Into the Woods), alignmentWithProtagonists, both helper and adversary]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alignmentWithProtagonists Context triple: [The Witch (Into the Woods), alignmentWithProtagonists, both helper and adversary]
-
A.
protagonistAllegiance
chosen
Indicates the group, cause, or side with which the main character is aligned or to which they show loyalty.
-
B.
speciesAlignment
Indicates how closely related or compatible two species are in terms of traits, behavior, or evolutionary relationship.
-
C.
alignedAgainst
Indicates that two or more entities are united in opposition to a common target, side, or objective.
-
D.
hasProtagonistRelationship
Indicates that there exists a central, story-driving relationship involving the protagonist and another entity within a narrative.
-
E.
coProtagonist
Indicates that two or more entities share the primary leading role together in the same narrative work.
- 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_69c0083179548190b384b0bf3c08ca4d |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02b52663c8190ab44258468d4296d |
completed | March 22, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69c021ca61688190875bd6107161c284 |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:48 p.m.