Triple
T3536576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Batman |
E74786
|
entity |
| Predicate | placeOfActivity |
P1527
|
FINISHED |
| Object | Gotham City |
E127940
|
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: Gotham City | Statement: [Batman, placeOfActivity, Gotham City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gotham City Context triple: [Batman, placeOfActivity, Gotham City]
-
A.
Gotham City
chosen
Gotham City is the dark, crime-ridden fictional metropolis that serves as Batman’s primary setting and symbolizes urban corruption and decay in the DC Comics universe.
-
B.
Star City
Star City is a commonly used nickname for the city of Lincoln, Nebraska.
-
C.
Gem City
Gem City is the well-known nickname for Dayton, Ohio, reflecting the city's historic prosperity and regional significance.
-
D.
Gotham
Gotham is a television crime drama series that explores the origins of Batman’s iconic allies and villains in a gritty, pre-Batman version of the infamous DC Comics city.
-
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 (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_69ad85d1a3948190931fd1ea1f49717b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbcc7b92481908d2d99948780f4d0 |
completed | March 8, 2026, 6:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b38bd237e881909df210a42346b572 |
completed | March 13, 2026, 4 a.m. |
Created at: March 8, 2026, 3:20 p.m.