Triple
T7934903
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IKE Phase 2 |
E184262
|
entity |
| Predicate | messageCount |
P35732
|
FINISHED |
| Object | three-message exchange in IKEv1 Quick Mode |
—
|
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: three-message exchange in IKEv1 Quick Mode | Statement: [IKE Phase 2, messageCount, three-message exchange in IKEv1 Quick Mode]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: messageCount Context triple: [IKE Phase 2, messageCount, three-message exchange in IKEv1 Quick Mode]
-
A.
payloadCount
chosen
Indicates the number of payload items associated with or carried by a given entity or operation.
-
B.
hasMessageNumber
Indicates that an entity is associated with a specific message identified by a particular number in a sequence or set.
-
C.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
D.
bookmarkCount
Indicates the number of times an item has been bookmarked by users.
-
E.
transactionCount
Indicates the number of transactions that have occurred involving the specified entity or between the related entities.
- 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_69ca8290c21c8190906a5ca6fe2b03c4 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3aec394081909a9569c02ac372af |
completed | March 31, 2026, 3:09 a.m. |
| PD | Predicate disambiguation | batch_69cae9335f288190ba96781fd6576a2b |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:08 p.m.