Triple
T23100469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wonder Woman |
E576008
|
entity |
| Predicate | baseOfOperations |
P2591
|
FINISHED |
| Object | Gateway City |
—
|
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: Gateway City | Statement: [Wonder Woman, baseOfOperations, Gateway City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gateway City Context triple: [Wonder Woman, baseOfOperations, Gateway City]
-
A.
Gateway City
Gateway City is a nickname for St. Louis, Missouri, highlighting its historic role as a major entry point to the American West.
-
B.
Gateway City
chosen
Gateway City is a fictional American metropolis in DC Comics best known as one of Wonder Woman’s primary cities of operation.
-
C.
Gateway City
Gateway City is a nickname for Geelong, a major port and industrial city in the Australian state of Victoria.
-
D.
Gateway City
Gateway City is the popular nickname of Sagamu, a major commercial and transport hub in southwestern Nigeria.
-
E.
Hat City
Hat City is the nickname of Danbury, Connecticut, reflecting its historic prominence as a major center of hat manufacturing in the United States.
- 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_69f18de8c5b4819095cddf989cade60d |
completed | April 29, 2026, 4:49 a.m. |
Created at: April 17, 2026, 3:58 p.m.