Triple
T52480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lick |
E1030
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | IBM |
E1102
|
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: IBM | Statement: [Lick, employer, IBM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: IBM Context triple: [Lick, employer, IBM]
-
A.
IBM
chosen
IBM is a multinational technology and consulting company known for its pioneering work in computer hardware, software, and enterprise services.
-
B.
Hewlett-Packard
Hewlett-Packard is a pioneering American technology company known for its innovations in computing, printers, and enterprise IT solutions.
-
C.
Intel Corporation
Intel Corporation is a leading American semiconductor company best known for designing and manufacturing microprocessors that power the majority of the world’s personal computers and servers.
-
D.
Microsoft
Microsoft is a multinational technology company best known for its Windows operating system, Office productivity suite, and Azure cloud computing platform.
-
E.
Oracle Corporation
Oracle Corporation is a major American multinational technology company best known for its database software, cloud services, and enterprise software products.
- 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_69a2480baefc81909951b14058479aa2 |
completed | Feb. 28, 2026, 1:42 a.m. |
| NER | Named-entity recognition | batch_69a24b0382b48190a7ca80ade6d2e270 |
completed | Feb. 28, 2026, 1:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a284f9fcd48190a3331f06d5dc00e8 |
completed | Feb. 28, 2026, 6:02 a.m. |
Created at: Feb. 28, 2026, 1:47 a.m.