Triple
T13150767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mid-Columbia region |
E312458
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Pasco |
E311812
|
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: Pasco | Statement: [Mid-Columbia region, hasCity, Pasco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pasco Context triple: [Mid-Columbia region, hasCity, Pasco]
-
A.
Pasco
Pasco is a station on Buenos Aires’ historic Line A subway, serving the Balvanera neighborhood in Argentina’s capital.
-
B.
Pasco
chosen
Pasco is a city in southeastern Washington State that forms part of the Tri-Cities region along with Kennewick and Richland.
-
C.
Eustis
Eustis is a surname of English origin borne by various notable individuals, including military figures and public officials in American history.
-
D.
Lakeland
Lakeland is a residential neighborhood located within the city of College Park in Prince George's County, Maryland.
-
E.
Bartow
Bartow is a small city in central Florida that serves as the county seat of Polk County and a regional hub for government and commerce.
- 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_69d806aabde48190899e13e41659cae5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98bd1fc408190b4b5ca973bcee403 |
completed | April 10, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6eaea3f888190b47ed7bf1c52e7e7 |
completed | May 3, 2026, 6:27 a.m. |
Created at: April 9, 2026, 9:11 p.m.