Triple
T24631690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal College of Anaesthetists |
E609691
|
entity |
| Predicate | hasSpecialtyFaculty |
P141
|
FINISHED |
| Object | Faculty of Pain Medicine |
—
|
NE NERFINISHED |
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: Faculty of Pain Medicine | Statement: [Royal College of Anaesthetists, hasSpecialtyFaculty, Faculty of Pain Medicine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpecialtyFaculty Context triple: [Royal College of Anaesthetists, hasSpecialtyFaculty, Faculty of Pain Medicine]
-
A.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
B.
hasFacultyIn
Indicates that an institution or organization has faculty members associated with or working in a particular department, field, or academic unit.
-
C.
hasFaculty
chosen
Indicates that an institution or department possesses or is associated with one or more faculty members.
-
D.
hasSpecialist
Indicates that one entity is associated with or assigned to a specialist entity that provides expert support, service, or oversight for it.
-
E.
hasFacultyType
Indicates that a faculty member or academic unit is associated with a specific category or type of faculty (e.g., full-time, adjunct, visiting).
- 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_69e2c4d1d3708190a0f2dc6a3a8523bb |
completed | April 17, 2026, 11:40 p.m. |
| NER | Named-entity recognition | batch_69f2be064ff88190b5d9e5ec75a41242 |
completed | April 30, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f2a6d0ab708190b2e3b94dd20ca76b |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:32 a.m.