Triple
T6973093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wipro Limited |
E161643
|
entity |
| Predicate | hasMajorBusinessArea |
P16009
|
FINISHED |
| Object | IT consulting |
—
|
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: IT consulting | Statement: [Wipro Limited, hasMajorBusinessArea, IT consulting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMajorBusinessArea Context triple: [Wipro Limited, hasMajorBusinessArea, IT consulting]
-
A.
hasMajorBusinessLine
chosen
Indicates that an entity conducts a primary or significant line of business in a specified area, sector, or activity.
-
B.
primaryBusinessArea
Indicates the main field, sector, or domain in which an entity primarily conducts its business activities.
-
C.
hasKeyBusinessArea
Indicates that an entity is associated with or operates within a particular primary business area or domain.
-
D.
hasBusiness
Indicates that one entity owns, operates, or is formally associated with a business entity.
-
E.
hasMajorEmployer
Indicates that an entity has a primary or most significant employer with which it is chiefly affiliated for work or occupation.
- 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_69c68854a0d88190bc0bf82263f1afce |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db3aad108190b19df2d21f5ce168 |
completed | March 27, 2026, 7:32 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c262508190a7708b3d9cf23d7c |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:30 p.m.