Triple
T3515043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biloxi |
E74285
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Gulfport |
E86848
|
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: Gulfport | Statement: [Biloxi, adjacentTo, Gulfport]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gulfport Context triple: [Biloxi, adjacentTo, Gulfport]
-
A.
Gulfport, Mississippi
chosen
Gulfport, Mississippi is a coastal city on the Gulf of Mexico known for its port, beaches, and role as a major urban center in southern Mississippi.
-
B.
Biloxi
Biloxi is a coastal Mississippi city known for its beaches, casinos, and seafood industry along the Gulf of Mexico.
-
C.
Pascagoula
Pascagoula is a coastal city in southeastern Mississippi known for its major shipbuilding industry and location along the Gulf of Mexico.
-
D.
Orange Beach
Orange Beach is a coastal resort city on Alabama’s Gulf Coast known for its white-sand beaches, boating, and vacation attractions.
-
E.
Gulf Shores
Gulf Shores is a popular coastal resort city on Alabama’s Gulf of Mexico shoreline, known for its white-sand beaches and 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_69ad85cfb5c881909c9a2edd9d6043cc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc30362c81908ca7497a6a935cc6 |
completed | March 8, 2026, 6:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b402d3a06481908e512c09306a2566 |
completed | March 13, 2026, 12:28 p.m. |
Created at: March 8, 2026, 3:19 p.m.