Triple
T277224
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SQL |
E5275
|
entity |
| Predicate | originatedAt |
P1041
|
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: [SQL, originatedAt, IBM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: IBM Context triple: [SQL, originatedAt, 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.
Sun Microsystems
Sun Microsystems was a pioneering American technology company best known for developing the Java programming language, the Solaris operating system, and high-performance networked computer systems.
-
D.
Compaq
Compaq was a major American computer company best known for its popular line of personal computers and for being one of the largest PC manufacturers before its acquisition by Hewlett-Packard.
-
E.
Micros Systems
Micros Systems was a leading provider of point-of-sale and hospitality management software and hardware solutions for restaurants, hotels, and retail businesses.
- 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_69a257e6c8788190987dfe705ca2912a |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25ded68c88190b1fc595ce329aeb9 |
completed | Feb. 28, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3faf81e108190b85040e8de93bfcb |
completed | March 1, 2026, 8:38 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.