Triple
T7126198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seattle |
E166067
|
entity |
| Predicate | hostedOfficeOf |
P1268
|
FINISHED |
| Object | Microsoft (nearby Redmond headquarters) |
—
|
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: Microsoft (nearby Redmond headquarters) | Statement: [Seattle, hostedOfficeOf, Microsoft (nearby Redmond headquarters)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hostedOfficeOf Context triple: [Seattle, hostedOfficeOf, Microsoft (nearby Redmond headquarters)]
-
A.
hosted
Indicates that one entity organized and provided the venue or platform for an event, activity, or presence involving another entity.
-
B.
hasOffice
chosen
Indicates that an entity possesses or maintains an office at a particular location or within a specific organization.
-
C.
hasOfficeType
Indicates that an entity’s office is classified as a specific type or category of office.
-
D.
aboutOfficeHeld
Indicates that one entity is related to, or provides information about, a specific office or position that is or was held by another entity.
-
E.
officeHeldOf
Indicates that a specific office or position is (or was) held by a particular person or entity.
- 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_69c6888350588190870cd552b427a1cd |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e64d99888190a93c1822e19b5457 |
completed | March 27, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69c6e1c7289881909f3b533c384f9ed4 |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:44 p.m.