Triple
T9266924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London 2012 Olympic Torch Relay |
E222723
|
entity |
| Predicate | torchBearerCount |
P87878
|
FINISHED |
| Object | approximately 8000 |
—
|
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: approximately 8000 | Statement: [London 2012 Olympic Torch Relay, torchBearerCount, approximately 8000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: torchBearerCount Context triple: [London 2012 Olympic Torch Relay, torchBearerCount, approximately 8000]
-
A.
torchBearerAt
Indicates that an entity is serving as the designated carrier of a torch at a specific place or time.
-
B.
tierCount
Indicates the number of distinct levels, ranks, or layers associated with an entity in a hierarchical or tiered structure.
-
C.
bootCapacity
Indicates the storage volume or carrying capacity available in the boot (trunk) of a vehicle.
-
D.
numberOfCapsules
Indicates the quantity or count of capsules associated with an entity or event.
-
E.
benchCount
Indicates the number of benches associated with a given entity or location.
- 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_69ca841f2e808190a64f4c31903a1332 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd074bac9481909419988a9e8d9bd5 |
completed | April 1, 2026, 11:53 a.m. |
| PD | Predicate disambiguation | batch_69cc7a537bbc8190baee71f556e52a7b |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc95597be081908ece2491dd2f0f74 |
completed | April 1, 2026, 3:47 a.m. |
Created at: March 30, 2026, 7:33 p.m.