Triple
T2175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York City |
E40
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Gotham |
E40
|
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 | Statement: [New York City, nickname, Gotham]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gotham Context triple: [New York City, nickname, Gotham]
-
A.
Chocolate City
Chocolate City is a popular nickname for Washington, D.C., highlighting its historically large and influential African American population and culture.
-
B.
Mystic River
Mystic River is a tidal estuary in the Greater Boston area of Massachusetts, historically significant for shipbuilding and industrial activity along its banks.
-
C.
Carnegie
Carnegie is a Scottish surname most famously associated with industrialist and philanthropist Andrew Carnegie.
-
D.
New York City
chosen
New York City is the largest city in the United States, a global center of finance, culture, media, and technology.
-
E.
Hollywood
Hollywood is a famous Los Angeles neighborhood internationally recognized as the historic center of the American film and entertainment industry.
- 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_69a22cde80848190b62c5f556b4d62ba |
completed | Feb. 27, 2026, 11:46 p.m. |
| NER | Named-entity recognition | batch_69a230c560548190a57df2421e233775 |
completed | Feb. 28, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a243c57fe481909b6c1b8f41757f96 |
completed | Feb. 28, 2026, 1:24 a.m. |
Created at: Feb. 27, 2026, 11:48 p.m.