Triple
T6281213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal 4S |
E140785
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | T4S |
E140785
|
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: T4S | Statement: [Terminal 4S, alsoKnownAs, T4S]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: T4S Context triple: [Terminal 4S, alsoKnownAs, T4S]
-
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 a light rail/tram line of the Trambesòs network serving the Barcelona metropolitan area.
-
D.
T4
T4 is a tram line that forms part of the urban light rail network serving the city of Casablanca, Morocco.
-
E.
Terminal 4S
chosen
Terminal 4S is the satellite terminal of Madrid’s Adolfo Suárez Madrid–Barajas Airport, primarily serving international and long-haul flights with modern, high-capacity facilities.
- 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_69c008cd17c8819082b82d3fbeb68047 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063dee62881908347283f16dcbe68 |
completed | March 22, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c51962132881909a2eccd1203e03c1 |
completed | March 26, 2026, 11:32 a.m. |
Created at: March 22, 2026, 4:26 p.m.