Triple
T21039255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queenie Goldstein |
E518274
|
entity |
| Predicate | loveInterest |
P7325
|
FINISHED |
| Object | Jacob Kowalski |
—
|
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: Jacob Kowalski | Statement: [Queenie Goldstein, loveInterest, Jacob Kowalski]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jacob Kowalski Context triple: [Queenie Goldstein, loveInterest, Jacob Kowalski]
-
A.
Jacob Kowalski
chosen
Jacob Kowalski is a kind-hearted No-Maj baker who becomes a close friend and ally to Newt Scamander in the Fantastic Beasts film series.
-
B.
Kowalski
Kowalski is a surname of Polish origin commonly used in Poland and among Polish diaspora communities.
-
C.
Kowalski
Kowalski is the intelligent, analytically minded penguin from the "Madagascar" franchise, known for serving as the team's strategist and inventor.
-
D.
Ray Kowalski
Ray Kowalski is a fictional Chicago detective who becomes Constable Benton Fraser’s streetwise and impulsive partner in the Canadian television series "Due South."
-
E.
Walt Kowalski
Walt Kowalski is a gruff, widowed Korean War veteran whose evolving relationship with his Hmong neighbors drives the emotional and moral core of the film "Gran Torino."
- 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_69e0b50438e08190917e2538bb8bc034 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fcee13b08190a8b3372f6759cd1b |
completed | April 21, 2026, 4:28 a.m. |
Created at: April 16, 2026, 2:13 p.m.