Triple
T442435
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doc |
E10140
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Doc |
E10140
|
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: Doc | Statement: [Doc, name, Doc]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Doc Context triple: [Doc, name, Doc]
-
A.
Doc
chosen
Doc is the widely used nickname of Glenn "Doc" Rivers, a former NBA player and championship-winning head coach.
-
B.
Doug
Doug is a common English masculine given name, typically used as a short form of Douglas.
-
C.
Charlie Y. Reader
Charlie Y. Reader is the central protagonist of the romantic comedy film "The Tender Trap," around whom the story’s romantic entanglements and personal growth revolve.
-
D.
Dave
Dave is a common masculine given name, often a shortened form of David, used widely in English-speaking countries.
-
E.
Michael V. Drake
Michael V. Drake is an American academic leader and physician who has served as president of both The Ohio State University and the University of California system.
- 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_69a2e8465ef481909655c681b01e2986 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2ef42b4008190abed9d79926c7022 |
completed | Feb. 28, 2026, 1:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a43e73555481909fc972eed3337945 |
completed | March 1, 2026, 1:26 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.