Triple
T498329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Delphi |
E10344
|
entity |
| Predicate | developer |
P73
|
FINISHED |
| Object | Borland |
E39942
|
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: Borland | Statement: [Delphi, developer, Borland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Borland Context triple: [Delphi, developer, Borland]
-
A.
Borland
chosen
Borland was a prominent software company best known for its influential development tools and programming environments, particularly during the 1980s and 1990s.
-
B.
Embarcadero
Embarcadero is a historic waterfront district in San Francisco known for its piers, ferry terminal, and scenic promenade along the bay.
-
C.
Turbo Pascal
Turbo Pascal is a once-popular integrated development environment and compiler for the Pascal programming language, known for its fast compilation speed and influence on early PC software development.
-
D.
Delphi
Delphi is an ancient Greek sanctuary and archaeological site famed for the Oracle of Apollo and its central role in classical Greek religion and culture.
-
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_69a2e847df8481909239ec08ccf1e376 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1183e988190bce70932a9678134 |
completed | Feb. 28, 2026, 1:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a481efc3a881909575c5981e13a16b |
completed | March 1, 2026, 6:14 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.