Triple
T15015747
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carre's Grammar School |
E377954
|
entity |
| Predicate | hasSpecialistStatusHistory |
P6973
|
FINISHED |
| Object | sports college |
—
|
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: sports college | Statement: [Carre's Grammar School, hasSpecialistStatusHistory, sports college]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpecialistStatusHistory Context triple: [Carre's Grammar School, hasSpecialistStatusHistory, sports college]
-
A.
hasSpecialistStatus
Indicates that an entity holds a recognized specialist designation or status in a particular field, role, or context.
-
B.
hasSpecialist
Indicates that one entity is associated with or assigned to a specialist entity that provides expert support, service, or oversight for it.
-
C.
hadSpecialStatusIn
chosen
Indicates that an entity possessed a particular special, exceptional, or non-standard status within a specified context or time period.
-
D.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
E.
hasSpecialistLists
Indicates that an entity maintains or is associated with one or more curated lists of specialists or specialized items.
- 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_69d85cd3a3c881908c71fc424d459c17 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7623c3c819092ca36b358b01842 |
completed | April 15, 2026, 12:10 a.m. |
| PD | Predicate disambiguation | batch_69de9a6531a88190acde65199a477350 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:55 a.m.