Triple
T29017588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Worlds of If |
E737357
|
entity |
| Predicate | HugoAwardBestProzineWinYears |
P165916
|
FINISHED |
| Object | 1966 |
—
|
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: 1966 | Statement: [Worlds of If, HugoAwardBestProzineWinYears, 1966]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: HugoAwardBestProzineWinYears Context triple: [Worlds of If, HugoAwardBestProzineWinYears, 1966]
-
A.
HugoAwardBestProzineWins
chosen
Indicates that an entity has won the Hugo Award for Best Fanzine/Prozine in a given year or context.
-
B.
numberOfHugoAwardsForBestProfessionalEditor
Indicates the count of Hugo Awards received for the role of Best Professional Editor.
-
C.
retroHugoAwardedForYearsWithoutHugos
Indicates that Retro Hugo Awards are given to recognize works from years in which no original Hugo Awards were presented.
-
D.
HugoAwardBestGraphicStoryWins
Indicates that an entity has won the Hugo Award for Best Graphic Story.
-
E.
returnedAwardYear
Indicates the year in which an award that had previously been given was returned or relinquished.
- 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_69f077ee19f881909af48f9cab00a2e5 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f65fe293188190a510c1d54a2bf542 |
completed | May 2, 2026, 8:34 p.m. |
| PD | Predicate disambiguation | batch_69f65c2376a08190be5215171e908e69 |
completed | May 2, 2026, 8:18 p.m. |
Created at: April 28, 2026, 9:46 a.m.