Triple
T16825555
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Signaling System No. 7 |
E409009
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | SS7 |
E409008
|
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: SS7 | Statement: [Signaling System No. 7, alsoKnownAs, SS7]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SS7 Context triple: [Signaling System No. 7, alsoKnownAs, SS7]
-
A.
SS7
chosen
SS7 (Signaling System No. 7) is a global telecommunications signaling protocol suite used to set up and manage telephone calls, SMS, and other network services across public switched telephone networks.
-
B.
SS7 Via Appia
SS7 Via Appia is a major Italian state highway that follows the historic route of the ancient Appian Way, connecting Rome with southern regions of the country.
-
C.
Signaling System No. 7
Signaling System No. 7 is a global telecommunications signaling protocol suite used to set up and manage telephone calls and other services across digital networks.
-
D.
GSM-R
GSM-R is a digital radio communication system used across European railways to provide secure voice and data links between trains and railway control centers.
-
E.
ISUP
ISUP is a French higher education institution in Paris specializing in advanced studies and training in statistics and actuarial science.
- 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_69d88394566c8190b3dcbdc72935f7fa |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b3126db88190ac5595b0d50e4232 |
completed | April 18, 2026, 4:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b29e48f881908489bd77a9caec97 |
completed | May 10, 2026, 4:30 p.m. |
Created at: April 10, 2026, 5:23 a.m.