Triple
T5032429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Capital Beltway region |
E113336
|
entity |
| Predicate | hasCommonReference |
P60630
|
FINISHED |
| Object | D.C. suburbs |
—
|
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: D.C. suburbs | Statement: [Capital Beltway region, hasCommonReference, D.C. suburbs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommonReference Context triple: [Capital Beltway region, hasCommonReference, D.C. suburbs]
-
A.
hasCommonValue
Indicates that two or more entities share at least one identical value or attribute in common.
-
B.
hasCommonRepresentative
Indicates that two or more entities share the same person or organization acting as their representative.
-
C.
sharesCommonAncestorWith
Indicates that two entities have at least one ancestor in common in their lineage or hierarchy.
-
D.
hasNearbyCommon
Indicates that two entities share at least one common element, feature, or connection that is located within a specified nearby distance or vicinity.
-
E.
hasReferenceID
Indicates that an entity is associated with a specific reference identifier used to uniquely track or distinguish it.
- F. None of above. chosen
Provenance (4 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_69bd443775e48190a646ffbfc4334723 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73b4d1708190aa1fa63104555f8a |
completed | March 20, 2026, 4:20 p.m. |
| PD | Predicate disambiguation | batch_69bd71509e9c8190a60c1d8d04936a12 |
completed | March 20, 2026, 4:09 p.m. |
| PDg | Predicate description generation | batch_69bd724ff2b4819091351cf80d3647a1 |
completed | March 20, 2026, 4:14 p.m. |
Created at: March 20, 2026, 1:36 p.m.