Triple
T6408725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Large Electron–Positron Collider |
E127652
|
entity |
| Predicate | tunnelReuse |
P18707
|
FINISHED |
| Object | used for Large Hadron Collider |
—
|
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: used for Large Hadron Collider | Statement: [Large Electron–Positron Collider, tunnelReuse, used for Large Hadron Collider]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tunnelReuse Context triple: [Large Electron–Positron Collider, tunnelReuse, used for Large Hadron Collider]
-
A.
usesTunnel
chosen
Indicates that one entity makes use of a tunnel as a passage or route to reach or connect to another entity.
-
B.
tunnelType
Indicates the specific kind or classification of a tunnel associated with an entity.
-
C.
hasTunnel
Indicates that one entity possesses, contains, or is connected by a tunnel to another entity.
-
D.
tunnelName
Indicates the name assigned to a specific tunnel in the relationship or data model.
-
E.
numberOfTunnels
Indicates the quantity of tunnels associated with or passing through a given entity or location.
- 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_69c0083723d88190b1e37b19df162c08 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c068cdf25881908d42a5d979637ad6 |
completed | March 22, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69c060f40ecc8190b1df17b96767675c |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:41 p.m.