Triple
T38030134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dausa railway station |
E948884
|
entity |
| Predicate | hasMailTrains |
P207564
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Dausa railway station, hasMailTrains, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMailTrains Context triple: [Dausa railway station, hasMailTrains, yes]
-
A.
hasTrains
Indicates that one entity possesses, operates, or is served by one or more trains in relation to another entity or context.
-
B.
hasLNGTrain
Indicates that something possesses or is equipped with an LNG (liquefied natural gas) processing or transport train as part of its facilities or infrastructure.
-
C.
hasRail
Indicates that something is equipped with, includes, or is connected to a rail or rail system.
-
D.
hasTailTrain
Indicates that one entity possesses or is characterized by a tail-like train extending from it.
-
E.
hasTramTrainLine
Indicates that there exists a tram-train line connection or service linking the related entities.
- F. None of above. chosen
Provenance (4 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_69f76efd1bc48190a729097fe5177b61 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037df1223c8190a5d61e4f8e6fd613 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a037a1ad6c48190bfe35d350c1b4751 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037df009f4819082e04683e6e8a106 |
completed | May 12, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:20 p.m.