Triple
T37863046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chetak |
E944386
|
entity |
| Predicate | legInjury |
P71097
|
FINISHED |
| Object | was gravely wounded in a leg during the Battle of Haldighati |
—
|
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: was gravely wounded in a leg during the Battle of Haldighati | Statement: [Chetak, legInjury, was gravely wounded in a leg during the Battle of Haldighati]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legInjury Context triple: [Chetak, legInjury, was gravely wounded in a leg during the Battle of Haldighati]
-
A.
injuredBodyPart
chosen
Indicates that an entity has sustained an injury specifically affecting a particular body part.
-
B.
losesLegIn
Indicates that an entity suffers the loss or amputation of a leg as a result of an event, situation, or location specified by the related entity.
-
C.
legs
Indicates that an entity possesses legs, specifying the presence or number of leg-like appendages associated with it.
-
D.
injuryType
Indicates the specific kind or category of injury associated with an entity or event.
-
E.
groundInjuries
Indicates that an entity has sustained injuries as a result of contact with or impact against the ground.
- 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_69f76eee2f9c8190b1272aa2ee55ebf5 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbbae559a8819086ef839973f8d9b2 |
completed | May 6, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69fbb1440fa08190abf25ba684f75b6e |
completed | May 6, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:19 p.m.