Triple
T6157178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belmar |
E137349
|
entity |
| Predicate | laterName |
P65
|
FINISHED |
| Object | Belmar |
E137349
|
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: Belmar | Statement: [Belmar, laterName, Belmar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belmar Context triple: [Belmar, laterName, Belmar]
-
A.
Belmar
chosen
Belmar is a popular coastal borough in Monmouth County, New Jersey, known for its sandy beaches, boardwalk, and vibrant summer tourism scene.
-
B.
Belmar
Belmar is a prominent mixed-use shopping, dining, and entertainment district serving as a central urban hub in Lakewood, Colorado.
-
C.
Verona Beach
Verona Beach is the modern, stylized coastal city that serves as the backdrop for Baz Luhrmann’s 1996 film adaptation of Shakespeare’s "Romeo + Juliet."
-
D.
Egg Harbor
Egg Harbor is a small village and popular tourist destination on the shores of Lake Michigan in Door County, Wisconsin, known for its waterfront, resorts, and seasonal festivals.
-
E.
Sea Isle City
Sea Isle City is a coastal resort town on the Jersey Shore in New Jersey, known for its beaches, promenade, and seasonal tourism.
- 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_69c008a45d008190832a9e19f5d63406 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05d3177588190970a45af0d43b04c |
completed | March 22, 2026, 9:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1418ba6488190aaf8d3070555d60a |
completed | March 23, 2026, 1:35 p.m. |
Created at: March 22, 2026, 4:17 p.m.