Triple
T15493120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spider-Man |
E378746
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | Aunt May |
E421823
|
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: Aunt May | Statement: [Spider-Man, relative, Aunt May]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aunt May Context triple: [Spider-Man, relative, Aunt May]
-
A.
Aunt May
chosen
Aunt May is Peter Parker’s loving and morally grounded aunt who serves as a key emotional anchor and guiding influence in the Spider-Man stories.
-
B.
Mary Jane Watson
Mary Jane Watson is a central character in the Spider-Man franchise, best known as Peter Parker’s longtime love interest and a key emotional anchor in his story.
-
C.
Wanda Blake
Wanda Blake is a central character in the Spawn franchise, known as Al Simmons’ wife whose loss and later life without him drive much of his tragic, supernatural journey.
-
D.
Helen Burns
Helen Burns is a pious, patient schoolgirl in Charlotte Brontë’s "Jane Eyre" whose quiet strength and Christian forgiveness deeply influence the young Jane.
-
E.
Tante Ju
Tante Ju is the affectionate nickname for the Junkers Ju 52, a German three-engined transport aircraft widely used in the 1930s and during World War II.
- 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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03fad723481908d2aa33e8f065f2f |
completed | April 16, 2026, 1:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3660fc6c81908caf1729260a8338 |
completed | May 9, 2026, 1:28 p.m. |
Created at: April 10, 2026, 3:49 a.m.