Triple
T9845832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Good Witch of the South |
E239339
|
entity |
| Predicate | helps |
P1853
|
FINISHED |
| Object | Scarecrow |
E48999
|
NE 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: Scarecrow | Statement: [Good Witch of the South, helps, Scarecrow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scarecrow Context triple: [Good Witch of the South, helps, Scarecrow]
-
A.
Scarecrow
Scarecrow is a Batman supervillain and deranged psychiatrist who uses fear-inducing toxins to terrorize Gotham City.
-
B.
Scarecrow
Scarecrow is a 1973 American road drama film directed by Jerry Schatzberg and starring Gene Hackman and Al Pacino as drifters traveling across the United States.
-
C.
The Scarecrow
The Scarecrow is a crime novel by Michael Connelly featuring journalist Jack McEvoy investigating a serial killer who exploits digital technology to stalk his victims.
-
D.
The Scarecrow
chosen
The Scarecrow is a beloved fictional figure from L. Frank Baum’s Oz stories, known for his quest for a brain and his role as one of Dorothy’s loyal companions.
-
E.
Toyman
Toyman is a DC Comics supervillain known for using deadly toy-themed gadgets and elaborate traps to battle Superman in Metropolis.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca84e3f0c48190ada72a65ebd50efd |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb35ff7848190a8a717773d8654b9 |
completed | April 2, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1ead49b14819086a9bbd256f298a9 |
completed | April 5, 2026, 4:53 a.m. |
Created at: March 30, 2026, 8:34 p.m.