Triple
T11817292
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darryl Philbin |
E281033
|
entity |
| Predicate | laterDepartment |
P101469
|
FINISHED |
| Object | office upstairs |
—
|
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: office upstairs | Statement: [Darryl Philbin, laterDepartment, office upstairs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterDepartment Context triple: [Darryl Philbin, laterDepartment, office upstairs]
-
A.
department
Indicates that one entity functions as an organizational unit or division within another, typically larger, entity.
-
B.
basedInDepartment
Indicates that an entity operates or has its primary affiliation within a specific department.
-
C.
departmentNumber
Indicates the specific numeric code assigned to identify a particular department within an organization or system.
-
D.
laterOffice
Indicates that one office or term of office occurs chronologically after another office or term.
-
E.
departmentType
Indicates the classification or category of a department, specifying what kind of department it is.
- 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_69d6ab26aae88190b2489efcb2a24234 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a658f918819092c2db05fe2ab0ce |
completed | April 10, 2026, 7:27 a.m. |
| PD | Predicate disambiguation | batch_69d8a24e9a088190aff7932d1ff93dbf |
completed | April 10, 2026, 7:10 a.m. |
| PDg | Predicate description generation | batch_69d8a6574b7081908f7451d2bb233967 |
completed | April 10, 2026, 7:27 a.m. |
Created at: April 8, 2026, 9:42 p.m.