Triple
T11356991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hugh Skinner |
E268980
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Zog |
E582895
|
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: Zog | Statement: [Hugh Skinner, notableWork, Zog]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zog Context triple: [Hugh Skinner, notableWork, Zog]
-
A.
Zog
chosen
Zog is a children's picture book by Julia Donaldson, illustrated by Axel Scheffler, about an eager young dragon learning at dragon school.
-
B.
Zunz
Zunz is a German-Jewish surname most prominently associated with Leopold Zunz, a pioneering 19th-century scholar of Jewish studies and founder of the "Science of Judaism" movement.
-
C.
Zazas
The Zazas are an Iranian ethnic group primarily inhabiting eastern Turkey, known for speaking the Zaza (Dimili) language and maintaining distinct cultural traditions.
-
D.
Zardoz
Zardoz is a 1974 science fiction film directed by John Boorman, known for its surreal, dystopian vision and starring Sean Connery in one of his most unconventional roles.
-
E.
Zaar
Zaar is a West Chadic language spoken primarily in Bauchi State, Nigeria.
- 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_69d6aacbe18081909e5fadb50082dd96 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea419afc8190b3a93141d015ebdf |
completed | April 9, 2026, 6:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e543b3cbd88190bb479ac88f8ca710 |
completed | April 19, 2026, 9:05 p.m. |
Created at: April 8, 2026, 9:33 p.m.