Triple
T23108306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harda railway station |
E576237
|
entity |
| Predicate | hasDrinkingWaterFacility |
P105704
|
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: [Harda railway station, hasDrinkingWaterFacility, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDrinkingWaterFacility Context triple: [Harda railway station, hasDrinkingWaterFacility, yes]
-
A.
hasDrinkingWaterFacilities
chosen
Indicates that a place or facility provides access to safe drinking water sources or infrastructure for people to use.
-
B.
hasAblutionFacilities
Indicates that an entity provides or is equipped with facilities for performing ablution or ritual washing.
-
C.
hasWaterServiceFrom
Indicates that one entity receives its water supply or water-related services from another entity.
-
D.
hasCleaningFacility
Indicates that an entity provides or is equipped with a facility or service for cleaning.
-
E.
hasWaterUse
Indicates a relationship where one entity utilizes or consumes water for a particular purpose, process, or function.
- 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_69e245f4af548190898d434a64a1e774 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e0c7b9c8190b1160485eae87c9b |
completed | April 29, 2026, 4:50 a.m. |
| PD | Predicate disambiguation | batch_69ef89f020588190b43393e048e7eda3 |
completed | April 27, 2026, 4:08 p.m. |
Created at: April 17, 2026, 3:58 p.m.