Triple
T17289695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stade Toulousain |
E419751
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | ST |
E419751
|
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: ST | Statement: [Stade Toulousain, shortName, ST]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ST Context triple: [Stade Toulousain, shortName, ST]
-
A.
ST
ST is the common abbreviation for Sound Transit, the regional public transit agency serving the Seattle metropolitan area in Washington State.
-
B.
ST
chosen
ST is the common abbreviation for Stade Toulousain, a leading French rugby union club based in Toulouse.
-
C.
ST
ST is the reporting mark used by the Springfield Terminal Railway, a regional railroad in New England.
-
D.
ST
ST is the vehicle registration code used on license plates for vehicles registered in Croatia’s Split-Dalmatia County.
-
E.
ST
ST is the vehicle registration code used on license plates for vehicles registered in the Steinfurt district of Germany.
- 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_69d886db32608190a61e18862c5a8af6 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e43782b7ac8190b702567e9ccf9a35 |
completed | April 19, 2026, 2:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a017957c738819087341bcd51b55114 |
completed | May 11, 2026, 6:38 a.m. |
Created at: April 10, 2026, 5:40 a.m.