Triple
T19985995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lori Black |
E493932
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Ozma |
—
|
NE NERFINISHED |
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: Ozma | Statement: [Lori Black, notableWork, Ozma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ozma Context triple: [Lori Black, notableWork, Ozma]
-
A.
Ozma
chosen
Ozma is an American rock band known for blending power pop melodies with indie and alternative rock influences, often drawing comparisons to early Weezer.
-
B.
Princess Ozma
Princess Ozma is a fictional royal character from L. Frank Baum’s Oz book series, serving as the rightful ruler of the Land of Oz.
-
C.
Ozma of Oz
Ozma of Oz is the third book in L. Frank Baum’s Oz series, introducing Princess Ozma as a central character and following Dorothy’s adventures in the magical Land of Oz.
-
D.
Glinda the Good Witch
Glinda the Good Witch is a benevolent and powerful sorceress from L. Frank Baum’s Oz universe, best known for guiding Dorothy on her journey home.
-
E.
OZ
OZ is the IATA airline designator assigned to Asiana Airlines, a major South Korean carrier based in Seoul.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65d16f60c81909ba02c0a3429ecae |
completed | April 20, 2026, 5:06 p.m. |
Created at: April 11, 2026, 3:29 p.m.