Triple
T708663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ballston |
E14157
|
entity |
| Predicate | hasOfficeCluster |
P19058
|
FINISHED |
| Object | defense contractors |
—
|
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: defense contractors | Statement: [Ballston, hasOfficeCluster, defense contractors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOfficeCluster Context triple: [Ballston, hasOfficeCluster, defense contractors]
-
A.
hasOffice
Indicates that an entity possesses or maintains an office at a particular location or within a specific organization.
-
B.
hasOfficeType
Indicates that an entity’s office is classified as a specific type or category of office.
-
C.
includedOffice
Indicates that one office is contained within, or forms part of, another office or organizational unit.
-
D.
establishedOffice
Indicates that an entity created or set up an official office or place of operation.
-
E.
numberOfOffices
Indicates the total count of offices associated with a given entity.
- 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_69a493494ec48190ae6751683625a9ba |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a5c011948190b2cfccd8fe722742 |
completed | March 1, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69a4a4f0217081908268b3f47e72f8df |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a5bed20c81909ecc28bf42594e72 |
completed | March 1, 2026, 8:46 p.m. |
Created at: March 1, 2026, 7:36 p.m.