Triple
T7886248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pat Robertson |
E183110
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Pat |
E111527
|
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: Pat | Statement: [Pat Robertson, nickname, Pat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pat Context triple: [Pat Robertson, nickname, Pat]
-
A.
Pat
chosen
Pat is the commonly used short form of the given name Patrick, often used as a casual or familiar nickname.
-
B.
Pete
Pete is a common masculine given name, typically used as a familiar or informal form of the name Peter.
-
C.
Pete
Pete is a classic Disney cartoon villain, best known as Mickey Mouse’s burly, antagonistic foe in the Mickey Mouse franchise.
-
D.
Pete
Pete is a fictional character known as the son of Uncle Tom in Harriet Beecher Stowe’s anti-slavery novel "Uncle Tom’s Cabin."
-
E.
Pete
Pete is a central figure in Stephen Crane’s novella "Maggie: A Girl of the Streets," representing the rough, working-class masculinity of New York’s Bowery slums.
- 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_69ca828af6e48190a06ee7010d8f0e64 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb39d7e6308190b5a554385e83ff0d |
completed | March 31, 2026, 3:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5b9286c881909ec01cb899e71d42 |
completed | March 31, 2026, 5:28 a.m. |
Created at: March 30, 2026, 4:59 p.m.