Triple
T9577153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kanwar Yatra |
E231072
|
entity |
| Predicate | associatedVow |
P89916
|
FINISHED |
| Object | observing abstinence |
—
|
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: observing abstinence | Statement: [Kanwar Yatra, associatedVow, observing abstinence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedVow Context triple: [Kanwar Yatra, associatedVow, observing abstinence]
-
A.
associatedVice
Indicates a relationship where one entity is linked to or connected with a particular vice, wrongdoing, or morally negative behavior of another entity.
-
B.
associatedWithVerb
Indicates that one entity is connected or linked to another through some verb-based relationship or action.
-
C.
associatedSingle
Indicates a one-to-one association where an entity is linked to exactly one corresponding related entity.
-
D.
associatedWithSee
Indicates a relationship where one entity is contextually or functionally linked to another through the act or concept of seeing or visual observation.
-
E.
associatedMOS
Indicates a relationship where one entity is linked to or paired with a specific Military Occupational Specialty (MOS) code or role.
- 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_69ca848091c48190bc313d6620d09555 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99ad7d108190a0b8c975351ea727 |
completed | April 1, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69ccd59fd7408190b36831902e3f37f7 |
completed | April 1, 2026, 8:21 a.m. |
| PDg | Predicate description generation | batch_69ccd93e90048190a2b0d7c5c195ba98 |
completed | April 1, 2026, 8:37 a.m. |
Created at: March 30, 2026, 8:05 p.m.