Triple
T3733450
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Santiago de Compostela |
E79122
|
entity |
| Predicate | numberOfStaff |
P23565
|
FINISHED |
| Object | over 2000 academic staff |
—
|
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: over 2000 academic staff | Statement: [University of Santiago de Compostela, numberOfStaff, over 2000 academic staff]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStaff Context triple: [University of Santiago de Compostela, numberOfStaff, over 2000 academic staff]
-
A.
staffSize
chosen
Indicates the number of staff members associated with an entity.
-
B.
personnelStrength
Indicates the number or capacity of people assigned to or available for a particular unit, organization, or operation.
-
C.
hasEmployees
Indicates that one entity employs one or more other entities as its workers or staff.
-
D.
employsApproximateNumberOfPeople
Indicates that an entity employs a roughly estimated or approximate number of people, rather than an exact headcount.
-
E.
numberOfExecutives
Indicates the total count of executives associated with a given entity or context.
- 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_69ad8b0e4650819090ad7cef094285e8 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb2457f08190a6b94e9895fced2c |
completed | March 8, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_69adc04746588190b0dc535638f23546 |
completed | March 8, 2026, 6:30 p.m. |
Created at: March 8, 2026, 3:34 p.m.