Triple
T8114270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Freeport Harbour |
E189432
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Freeport |
E187138
|
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: Freeport | Statement: [Freeport Harbour, locatedIn, Freeport]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Freeport Context triple: [Freeport Harbour, locatedIn, Freeport]
-
A.
Freeport
chosen
Freeport is a major Bahamian city on Grand Bahama known as a free-trade zone and popular tourist and commercial hub.
-
B.
Freeport
Freeport is a small borough in western Pennsylvania known for its location along the Allegheny River and its historic role as a regional transportation and industrial hub.
-
C.
Freeport
Freeport is an unincorporated community located in Winneshiek County in the northeastern part of the U.S. state of Iowa.
-
D.
Freeport
Freeport is a small city in northern Illinois known as the county seat of Stephenson County and for its historic role in the Lincoln–Douglas debates.
-
E.
Freeport
Freeport is a waterfront village on Long Island in New York known for its marinas, fishing industry, and nautical 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_69ca82baad008190ab2859712b9b1607 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb432f2a24819097be6ab9b03567bd |
completed | March 31, 2026, 3:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cced122cf08190b9cf17c3055e5431 |
completed | April 1, 2026, 10:01 a.m. |
Created at: March 30, 2026, 5:32 p.m.