Triple
T17704125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unreliable Memoirs |
E441384
|
entity |
| Predicate | settingLocation |
P40
|
FINISHED |
| Object | Sydney |
—
|
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: Sydney | Statement: [Unreliable Memoirs, settingLocation, Sydney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sydney Context triple: [Unreliable Memoirs, settingLocation, Sydney]
-
A.
Sydney
chosen
Sydney is Australia's largest and most populous city, renowned for its iconic harbour, Opera House, and Harbour Bridge.
-
B.
Sydney
Sydney is the spirited, fashionable young woman who serves as the central heroine of Louisa May Alcott’s novel "An Old-Fashioned Girl."
-
C.
Sydney
Sydney is a unisex given name of Old English origin meaning "wide island" that is used in various English-speaking countries.
-
D.
Sydney
Sydney is a character in the British period drama series "Lark Rise to Candleford," which portrays life in two contrasting rural communities in late 19th-century England.
-
E.
Sydney
Sydney is the seasoned professional gambler and mentor who serves as the central character in Paul Thomas Anderson’s crime drama film "Hard Eight."
- 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_69d8b9ea20b48190ace88bb46b01e6a9 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47296770c8190b6b172fb647da564 |
completed | April 19, 2026, 6:13 a.m. |
Created at: April 10, 2026, 10:05 a.m.