Triple
T9969803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan in Real Life |
E196174
|
entity |
| Predicate | screenplayBy |
P15305
|
FINISHED |
| Object | Pierce Gardner |
E832231
|
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: Pierce Gardner | Statement: [Dan in Real Life, screenplayBy, Pierce Gardner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pierce Gardner Context triple: [Dan in Real Life, screenplayBy, Pierce Gardner]
-
A.
Pierce Gardner
chosen
Pierce Gardner is a screenwriter best known for co-writing the romantic comedy-drama film "Dan in Real Life."
-
B.
Nathan Gardner
Nathan Gardner is a person known primarily as a relative of Susan Gardner.
-
C.
Nathan Gardner
Nathan Gardner is an educational administrator who serves as a school principal.
-
D.
Ogden Morrow
Ogden Morrow is a key supporting character in Ernest Cline's novel "Ready Player One," known as the reclusive co-creator of the virtual reality world OASIS.
-
E.
Montgomery Brewster
Montgomery Brewster is the hapless minor-league baseball player who must spend a vast inheritance under strict conditions in the comedy film "Brewster's Millions."
- 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_69ca82eea2b88190a0e511d21a31f386 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb7b7ea9881908a56f11e2e446dd0 |
completed | April 2, 2026, 12:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d281ebf6f88190a94ba8231422c591 |
completed | April 5, 2026, 3:38 p.m. |
Created at: March 30, 2026, 8:48 p.m.