Triple
T32959794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Advanced Gas-cooled Reactor |
E843202
|
entity |
| Predicate | numberOfStationsBuilt |
P1301
|
FINISHED |
| Object | 7 twin-unit stations |
—
|
LITERAL 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: 7 twin-unit stations | Statement: [Advanced Gas-cooled Reactor, numberOfStationsBuilt, 7 twin-unit stations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStationsBuilt Context triple: [Advanced Gas-cooled Reactor, numberOfStationsBuilt, 7 twin-unit stations]
-
A.
numberOfStations
chosen
Indicates the total count of stations associated with or contained by a given entity.
-
B.
numberOfNewStations
Indicates the quantity of stations that are newly created, added, or introduced within a given context or time period.
-
C.
numberOfStationsOpenedInPhase1
Indicates the total count of stations that were opened during the first phase of a project or rollout.
-
D.
numberOfUndergroundStations
Indicates the total count of underground (subway/metro) stations associated with a given entity.
-
E.
numberOfTrainsetsBuilt
Indicates the total count of trainsets that have been constructed or produced.
- F. None of above.
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_69f3494af2808190ad98cec2f1bc0fe6 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a02206665508190ae2324d253ccf377 |
completed | May 11, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_6a021fdc6e54819082847bedd97a680b |
completed | May 11, 2026, 6:28 p.m. |
Created at: May 1, 2026, 1:21 a.m.