Triple
T23100318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Superman mythos |
E576005
|
entity |
| Predicate | hasPrimarySetting |
P14490
|
FINISHED |
| Object | Metropolis |
—
|
NE NERFINISHED |
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: Metropolis | Statement: [Superman mythos, hasPrimarySetting, Metropolis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Metropolis Context triple: [Superman mythos, hasPrimarySetting, Metropolis]
-
A.
Metropolis
Metropolis is a landmark 1927 German expressionist science-fiction film directed by Fritz Lang, renowned for its pioneering special effects and dystopian vision of a futuristic urban society.
-
B.
Metropolis
Metropolis is a major Ethereum protocol upgrade that introduced significant improvements to scalability, security, and usability of the blockchain.
-
C.
Metropolis
Metropolis is a science fiction television series inspired by Fritz Lang’s classic 1927 film, exploring a futuristic city divided by class and technological power.
-
D.
Metropolis
Metropolis is a historical crime novel by Philip Kerr featuring detective Bernie Gunther in a pre-World War II Berlin setting.
-
E.
Metropolis
chosen
Metropolis is a DC Comics–themed area in various Six Flags parks, styled as Superman’s iconic city with related rides and attractions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245c060b48190a9bd61a47a16db17 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18de81060819084ab618f05aae583 |
completed | April 29, 2026, 4:49 a.m. |
Created at: April 17, 2026, 3:58 p.m.