Triple
T7768957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tik-Tok of Oz |
E179020
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object | Betsy Bobbin |
E687528
|
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: Betsy Bobbin | Statement: [Tik-Tok of Oz, hasMainCharacter, Betsy Bobbin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Betsy Bobbin Context triple: [Tik-Tok of Oz, hasMainCharacter, Betsy Bobbin]
-
A.
Betsy Bobbin
chosen
Betsy Bobbin is a young American girl who becomes one of the child protagonists in L. Frank Baum’s Oz series, embarking on magical adventures alongside characters like Tik-Tok and the Shaggy Man.
-
B.
Clothespin
Clothespin is a large-scale public sculpture by Claes Oldenburg, resembling an oversized clothespin and exemplifying his playful transformation of everyday objects into monumental art.
-
C.
Betsy
Betsy is a common diminutive or nickname for the given name Elizabeth.
-
D.
Betsy
Betsy is a key female character in the 1976 film "Taxi Driver," known as the idealistic campaign worker who becomes the object of Travis Bickle’s fixation.
-
E.
Bündchen
Bündchen is the German-origin surname most famously borne by Brazilian supermodel Gisele Bündchen.
- 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_69c69f30602c819082ab52cd4af5c592 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c704376dc08190890f5ebb9f259cfd |
completed | March 27, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8d6d0f1988190b4df650bebb61fd3 |
completed | March 29, 2026, 7:37 a.m. |
Created at: March 27, 2026, 4:11 p.m.