Triple
T20654813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laurens, New York |
E507594
|
entity |
| Predicate | hasRegionCode |
P3446
|
FINISHED |
| Object | US-NY |
—
|
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: US-NY | Statement: [Laurens, New York, hasRegionCode, US-NY]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: US-NY Context triple: [Laurens, New York, hasRegionCode, US-NY]
-
A.
US-NY
chosen
US-NY is the region code for the U.S. state of New York.
-
B.
NYSA
NYSA is the commonly used abbreviation for the New York State Archives, the official repository for New York State government records and historical documents.
-
C.
NY
NY is the vehicle registration code used for cars registered in the Hungarian city of Nyíregyháza.
-
D.
N.D.N.Y.
N.D.N.Y. is the standard abbreviation for the United States District Court for the Northern District of New York, a federal trial court within the Second Circuit.
-
E.
New York State
New York State is a populous and economically significant state in the northeastern United States, known for its diverse landscapes, major cities like New York City, and central role in finance, culture, and politics.
- 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_69e0b4bf58c081908e52a4500e03ff83 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b2ec90e881909250884483429acf |
completed | April 20, 2026, 11:12 p.m. |
Created at: April 16, 2026, 11:43 a.m.