Triple
T11384365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JR Group |
E269677
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | JR |
E114274
|
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: JR | Statement: [JR Group, abbreviation, JR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: JR Context triple: [JR Group, abbreviation, 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.
RJ
RJ is the two-letter IATA airline designator assigned to Royal Jordanian, the flag carrier airline of Jordan.
-
D.
RJ
RJ is the crafty, fast-talking raccoon who leads the animal ensemble in the animated film "Over the Hedge."
-
E.
JT
JT is a lightweight 3D visualization and data exchange file format commonly used in CAD and PLM workflows for efficient sharing of complex product models.
- 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_69d6aacdbc6c8190af6dc3d5f5d22836 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7fc34f1f0819082dd977313ee6070 |
completed | April 9, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e58c1d4b188190b83cfad0cc95483e |
completed | April 20, 2026, 2:14 a.m. |
Created at: April 8, 2026, 9:34 p.m.