Triple
T20113514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | So Close |
E490392
|
entity |
| Predicate | notableWorkOf |
P4
|
FINISHED |
| Object | JR |
—
|
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: JR | Statement: [So Close, notableWorkOf, JR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: JR Context triple: [So Close, notableWorkOf, JR]
-
A.
JR
chosen
JR is a French street artist and photographer renowned for his large-scale public art installations that transform urban spaces and address social and political issues worldwide.
-
B.
JR
JR is a character from Alison Bechdel’s long-running comic strip "Dykes to Watch Out For," which chronicles the lives and relationships of a diverse group of lesbian friends.
-
C.
JR
JR is the station code assigned to J. Ruiz station in the Manila Metro Rail Transit system.
-
D.
JR
JR is the common brand name and logo used by the Japan Railways Group, a network of major passenger and freight railway companies in Japan.
-
E.
JR
JR is a complex, satirical novel by William Gaddis that critiques American capitalism and corporate culture through fragmented dialogue and dark humor.
- 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_69da62636cc08190982cc71733a17b8d |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e666e31af081908d8e0c867c388a73 |
completed | April 20, 2026, 5:48 p.m. |
Created at: April 11, 2026, 11:29 p.m.