Triple
T16072453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Loring |
E389896
|
entity |
| Predicate | appearsIn |
P795
|
FINISHED |
| Object | Juno |
E83220
|
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: Juno | Statement: [Mark Loring, appearsIn, Juno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Juno Context triple: [Mark Loring, appearsIn, Juno]
-
A.
Juno
chosen
Juno is a 2007 coming-of-age comedy-drama film about a witty teenager dealing with an unplanned pregnancy, widely praised for its sharp dialogue and performances.
-
B.
Juno
Juno is a NASA space probe designed to study Jupiter’s composition, gravity field, magnetic field, and polar magnetosphere to better understand the planet’s formation and evolution.
-
C.
Juno
Juno is a small unincorporated community located in Dawson County, Georgia, known for its rural character and scenic North Georgia setting.
-
D.
Juno
Juno is the nickname of Zbigniew Gęsicki, a notable figure known primarily for his role in the Polish resistance during World War II.
-
E.
Juno
Juno is the ancient Roman queen of the gods, associated with marriage, childbirth, and the protection of the state.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e183bf6c488190b0099a00f13f2a69 |
completed | April 17, 2026, 12:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe484cef08190a3797c91a7025081 |
completed | May 10, 2026, 1:51 a.m. |
Created at: April 10, 2026, 4:57 a.m.