Triple
T8433133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thanos |
E199162
|
entity |
| Predicate | alias |
P39
|
FINISHED |
| Object | The Mad Titan |
E199162
|
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: The Mad Titan | Statement: [Thanos, alias, The Mad Titan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Mad Titan Context triple: [Thanos, alias, The Mad Titan]
-
A.
Thanos
chosen
Thanos is a powerful cosmic warlord from Marvel Comics and the Marvel Cinematic Universe, obsessed with balancing the universe by wiping out half of all life using the Infinity Stones.
-
B.
The Hulkster
The Hulkster is the ring nickname of Hulk Hogan, the iconic professional wrestler and pop culture figure known for his larger-than-life persona in WWE during the 1980s and 1990s.
-
C.
Loki
Loki is a trickster god in Norse mythology known for his shape-shifting, cunning, and role in both aiding and undermining the other gods.
-
D.
Absorbing Man
Absorbing Man is a Marvel Comics supervillain, often depicted as a formidable foe of heroes like Thor and the Hulk, who can magically absorb the properties of anything he touches.
-
E.
Giant-Man
Giant-Man is a Marvel Comics superhero identity used by scientist Hank Pym when he grows to enormous size using his Pym Particle technology.
- 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_69ca8313c99081909a5c6d83b91de5b3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbd1a5d7488190842e246444fc9a4e |
completed | March 31, 2026, 1:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce1d673ed48190abf765c203ed2a0f |
completed | April 2, 2026, 7:40 a.m. |
Created at: March 30, 2026, 6:07 p.m.