Triple
T2820759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Western Cape Provincial Government |
E54801
|
entity |
| Predicate | numberOfMECs |
P43517
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [Western Cape Provincial Government, numberOfMECs, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMECs Context triple: [Western Cape Provincial Government, numberOfMECs, 10]
-
A.
numberOfDetectors
Indicates the quantity of detectors associated with or involved in a given entity or system.
-
B.
numberOfModules
Indicates the total count of modules associated with a given entity.
-
C.
numberOfInstances
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
-
D.
numberOfCells
Indicates the total count of individual cells associated with or contained in a given entity.
-
E.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
- 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_69ab49e100c0819082a40cb797383243 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abdf15b7288190a03d1193cc0544a6 |
completed | March 7, 2026, 8:17 a.m. |
| PD | Predicate disambiguation | batch_69abdd08f2f481908c3da8a9c7a00552 |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abdf13d2b8819097b8edaaea90dbe2 |
completed | March 7, 2026, 8:17 a.m. |
Created at: March 6, 2026, 9:59 p.m.