Triple
T3658357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Python |
E77588
|
entity |
| Predicate | slainAt |
P21
|
FINISHED |
| Object | Delphi |
E2400
|
NE FINISHED |
How this triple was built (3 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: Delphi | Statement: [Python, slainAt, Delphi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Delphi Context triple: [Python, slainAt, Delphi]
-
A.
Delphi
chosen
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.
-
B.
Delphi (programming language)
Delphi is an object-oriented, rapid application development programming language and environment derived from Pascal, primarily used for building native Windows applications.
-
C.
Embarcadero
Embarcadero is a historic waterfront district in San Francisco known for its piers, ferry terminal, and scenic promenade along the bay.
-
D.
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.
-
E.
Borland
Borland was a prominent software company best known for its influential development tools and programming environments, particularly during the 1980s and 1990s.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: slainAt Context triple: [Python, slainAt, Delphi]
-
A.
killedBy
Indicates that one entity caused the death of another entity.
-
B.
placeOfDeath
chosen
Indicates the location where an entity (typically a person or animal) died.
-
C.
discoveredAsDeadBy
Indicates that one entity was found to be dead by another entity, who discovered their deceased state.
-
D.
killedAt
Indicates that a killing event occurred at a specific location or time associated with the entities involved.
-
E.
diesAt
Indicates that an entity ceases to live or exist at a specific time or location.
- F. None of above.
Provenance (4 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_69ad85def5cc8190863dccf55a18bebb |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc3d48a2081908ac0f76d548a53ee |
completed | March 8, 2026, 6:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4884071c881909daae2f3c510d399 |
completed | March 13, 2026, 9:57 p.m. |
| PD | Predicate disambiguation | batch_69adb847e9d881909dad2ffd0f3b6c15 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:24 p.m.