Triple
T23782376
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Basantar |
E587849
|
entity |
| Predicate | armourLossesPakistan |
P153917
|
FINISHED |
| Object | dozens of tanks destroyed or captured |
—
|
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: dozens of tanks destroyed or captured | Statement: [Battle of Basantar, armourLossesPakistan, dozens of tanks destroyed or captured]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: armourLossesPakistan Context triple: [Battle of Basantar, armourLossesPakistan, dozens of tanks destroyed or captured]
-
A.
AfghanCasualties
Indicates the number or occurrence of casualties suffered by Afghan individuals or forces in a given event or context.
-
B.
aircraftLosses
Indicates the number or occurrence of aircraft that have been destroyed, damaged beyond repair, or otherwise lost.
-
C.
numberOfPakistaniPrisonersTaken
Indicates the quantity of Pakistani prisoners that were captured or taken into custody in a given context.
-
D.
militaryCasualtiesSide
Indicates the side or party in a conflict to which the recorded military casualties belong.
-
E.
casualtiesInflictedOn
Indicates that one party has caused deaths or injuries to another party as a result of a harmful event or action.
- 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_69e2490f4ad48190b690878eec3596c6 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1c62d7c608190b5fd0cf35f5faf42 |
completed | April 29, 2026, 8:49 a.m. |
| PD | Predicate disambiguation | batch_69f155f79e34819080f9ddb972b34deb |
completed | April 29, 2026, 12:51 a.m. |
| PDg | Predicate description generation | batch_69f15ed138f88190a8ae555422978908 |
completed | April 29, 2026, 1:28 a.m. |
Created at: April 17, 2026, 7:16 p.m.