Triple
T9471911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HP Smart app |
E228412
|
entity |
| Predicate | publisher |
P29
|
FINISHED |
| Object | HP Inc. |
E7429
|
NE 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: HP Inc. | Statement: [HP Smart app, publisher, HP Inc.]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HP Inc. Context triple: [HP Smart app, publisher, HP Inc.]
-
A.
Hewlett-Packard
chosen
Hewlett-Packard is a pioneering American technology company known for its innovations in computing, printers, and enterprise IT solutions.
-
B.
HP
HP is the vehicle registration code used on motor vehicles registered in the Indian state of Himachal Pradesh.
-
C.
HP
HP is a postcode area in the United Kingdom covering High Wycombe and surrounding parts of Buckinghamshire and nearby counties.
-
D.
Xerox
Xerox is an American corporation best known for pioneering photocopiers and influential computing innovations, including early graphical user interfaces and office software.
-
E.
Canon Inc.
Canon Inc. is a Japanese multinational corporation renowned for its imaging and optical products, including cameras, camcorders, printers, and related equipment.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca847162c48190b079076c9595513c |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7fef6f288190b2d158c829b31de9 |
completed | April 1, 2026, 8:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12ce6c7c88190825bf618f1f1f9b5 |
completed | April 4, 2026, 3:23 p.m. |
Created at: March 30, 2026, 7:54 p.m.