Triple
T2636433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lanham, Maryland |
E59756
|
entity |
| Predicate | nearMajorEmploymentCenter |
P19886
|
FINISHED |
| Object | Washington, D.C. federal offices |
—
|
LITERAL 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: Washington, D.C. federal offices | Statement: [Lanham, Maryland, nearMajorEmploymentCenter, Washington, D.C. federal offices]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearMajorEmploymentCenter Context triple: [Lanham, Maryland, nearMajorEmploymentCenter, Washington, D.C. federal offices]
-
A.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
B.
nearDowntown
Indicates that one location is situated close to or within a short distance of a city’s downtown area.
-
C.
locatedNearMetropolitanArea
chosen
Indicates that one entity is situated in close geographic proximity to a metropolitan (urban) area.
-
D.
nearMetroStation
Indicates that one entity is located close to or within a short walking distance of a metro (subway) station.
-
E.
hasNearbyLandUse
Indicates that one land area is located close to another area characterized by a specific type of land use.
- F. None of above.
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_69ab4ac8596c8190b34997e73d9e991c |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd8e1fffc81908e4921690098c8db |
completed | March 7, 2026, 7:50 a.m. |
| PD | Predicate disambiguation | batch_69abd812849881908f956845a80e0205 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:50 p.m.