Triple
T5771328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 3GPP TSG CT |
E127336
|
entity |
| Predicate | hasWorkingGroup |
P1382
|
FINISHED |
| Object | CT4 |
E523109
|
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: CT4 | Statement: [3GPP TSG CT, hasWorkingGroup, CT4]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CT4 Context triple: [3GPP TSG CT, hasWorkingGroup, CT4]
-
A.
CT4
chosen
CT4 is a 3GPP working group responsible for specifying protocols and interfaces for core network and terminal interoperability in mobile telecommunications systems.
-
B.
CT1
CT1 is a 3GPP working group responsible for specifying core network and terminal aspects of mobile telecommunications systems.
-
C.
CT3
CT3 is a 3GPP Core Network and Terminals working group responsible for developing and maintaining signaling protocols and related specifications for mobile telecommunications networks.
-
D.
T4
T4 is one of the lines of the Athens tram system, providing urban light-rail service across part of the Athens metropolitan area.
-
E.
T4
T4 is a tram line serving the city of Villeurbanne as part of the Lyon metropolitan public transport network in France.
- 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_69c00834f6308190851b0abeddd8ed7e |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029ac21ec81908d88ba72e966d7cb |
completed | March 22, 2026, 5:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07e648aa881908411a00d48998ecc |
completed | March 22, 2026, 11:42 p.m. |
Created at: March 22, 2026, 3:50 p.m.