Triple
T8378196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert L. May |
E197626
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Rudolph the Red-Nosed Reindeer |
E38319
|
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: Rudolph the Red-Nosed Reindeer | Statement: [Robert L. May, notableWork, Rudolph the Red-Nosed Reindeer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rudolph the Red-Nosed Reindeer Context triple: [Robert L. May, notableWork, Rudolph the Red-Nosed Reindeer]
-
A.
Rudolph
chosen
Rudolph is the legendary red-nosed reindeer from Christmas folklore who guides Santa Claus’s sleigh through the night.
-
B.
Rudolph
Rudolph is a surname of German origin borne by various notable individuals and families.
-
C.
Rudolph
Rudolph is the full given name of Rudy Giuliani, the former mayor of New York City and prominent American political figure.
-
D.
Frosty the Snowman
Frosty the Snowman is a popular Christmas character and song figure, depicted as a magically animated snowman who comes to life and has joyful winter adventures.
-
E.
The Grinch
The Grinch is a famously grouchy, green, Christmas-hating character from Dr. Seuss’s classic children’s story “How the Grinch Stole Christmas!”
- 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_69ca82f64c188190af4e1608036b865d |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb80c229708190b813f5e7e44e10d4 |
completed | March 31, 2026, 8:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce3967fd108190b045ea1328b1dc4b |
completed | April 2, 2026, 9:39 a.m. |
Created at: March 30, 2026, 6:02 p.m.