Triple
T2996881
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | On What There Is |
E81089
|
entity |
| Predicate | usesExample |
P1259
|
FINISHED |
| Object | Pegasus |
E251758
|
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: Pegasus | Statement: [On What There Is, usesExample, Pegasus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pegasus Context triple: [On What There Is, usesExample, Pegasus]
-
A.
Pegasus
Pegasus is the NATO reporting name for the Boeing KC-46, a modern aerial refueling and strategic military transport aircraft.
-
B.
Pegasus
chosen
Pegasus is the iconic winged horse that serves as the central emblem in TriStar Pictures' film studio logo.
-
C.
Bellerophon
Bellerophon is a hero of Greek mythology best known for taming the winged horse Pegasus and slaying the monstrous Chimera.
-
D.
Argus
Argus is an early distributed programming language known for pioneering concepts in fault-tolerant, distributed systems and influencing modern object-oriented and concurrent programming.
-
E.
Argus
Argus is a many-eyed giant from Greek mythology best known for his role as a vigilant guardian.
- 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_69ad8b187fc8819085914d3c9ea3142d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99f612148190a5a565ba2ecc4fc0 |
completed | March 8, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b12e419e508190be23a2d413357058 |
completed | March 11, 2026, 8:56 a.m. |
Created at: March 8, 2026, 2:59 p.m.