Triple
T14209132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tramway T4 |
E352179
|
entity |
| Predicate | lineNumber |
P1864
|
FINISHED |
| Object | T4 |
E253347
|
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: T4 | Statement: [Tramway T4, lineNumber, T4]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: T4 Context triple: [Tramway T4, lineNumber, T4]
-
A.
T4
T4 is one of the lines of the Athens tram system, providing urban light-rail service across part of the Athens metropolitan area.
-
B.
T4
T4 is a tram line serving the city of Villeurbanne as part of the Lyon metropolitan public transport network in France.
-
C.
T4
T4 is the large, modern main passenger terminal at Adolfo Suárez Madrid–Barajas Airport in Madrid, Spain, known for its distinctive architecture and extensive international flight operations.
-
D.
T4
T4 is the fourth passenger terminal at Melbourne Airport, serving as one of the airport’s main facilities for domestic and low-cost airline operations.
-
E.
T4
chosen
T4 is a light rail/tram line of the Trambesòs network serving the Barcelona metropolitan area.
- 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_69d8278a06e481908b5d6af0a8afe737 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61fa8d24819092a8ec5d34c1c799 |
completed | April 14, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd19557f908190abb3dc116676f215 |
completed | May 7, 2026, 10:59 p.m. |
Created at: April 10, 2026, 1:05 a.m.