Triple
T10304210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Model Checking |
E241708
|
entity |
| Predicate | topic |
P261
|
FINISHED |
| Object | LTL |
E824076
|
NE 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: LTL | Statement: [Model Checking, topic, LTL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LTL Context triple: [Model Checking, topic, LTL]
-
A.
LTL
LTL is the former official currency code for the Lithuanian litas, which was replaced by the euro in 2015.
-
B.
LTL
chosen
LTL (Linear Temporal Logic) is a formalism used in computer science and logic to specify and reason about the temporal ordering of events along linear time, particularly in the verification of reactive and concurrent systems.
-
C.
LTL
LTL is the National Rail station code for Littleborough railway station in Greater Manchester, England.
-
D.
TLT
TLT is the time zone abbreviation used for Timor Leste Time, the standard time observed in East Timor.
-
E.
TLA
TLA is a formal specification language developed by Leslie Lamport for describing and reasoning about concurrent and distributed systems using temporal logic.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d381ac38808190a8ca7457c85b625b |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d308f034819098da69b963eb8a02 |
completed | April 7, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71d58416081909a010e905d70e934 |
completed | April 9, 2026, 3:30 a.m. |
Created at: April 6, 2026, 11:45 a.m.