Triple
T41457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clarendon |
E816
|
entity |
| Predicate | postalAddressRegion |
P920
|
FINISHED |
| Object | ZIP codes associated with Arlington, Virginia |
—
|
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: ZIP codes associated with Arlington, Virginia | Statement: [Clarendon, postalAddressRegion, ZIP codes associated with Arlington, Virginia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: postalAddressRegion Context triple: [Clarendon, postalAddressRegion, ZIP codes associated with Arlington, Virginia]
-
A.
postalArea
chosen
Indicates that one entity is the postal or ZIP code area associated with the location or address represented by the other entity.
-
B.
regionName
Indicates the name assigned to a specific geographic or administrative region.
-
C.
hasRegion
Indicates that an entity includes, contains, or is associated with a specific geographic or administrative region as part of its scope or structure.
-
D.
postalCode
Indicates the numerical or alphanumerical code assigned to a geographic area for mail delivery associated with an entity.
-
E.
countrySubdivision
Indicates that one geopolitical region is an administrative or territorial subdivision of a larger country.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24db9527c8190816b6b25c88cb2f4 |
completed | Feb. 28, 2026, 2:06 a.m. |
| PD | Predicate disambiguation | batch_69a24ab8a8908190beec6da6694dd4c9 |
completed | Feb. 28, 2026, 1:54 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.