Triple
T16295326
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Meredith |
E395631
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Dandy Don |
E395631
|
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: Dandy Don | Statement: [Don Meredith, nickname, Dandy Don]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dandy Don Context triple: [Don Meredith, nickname, Dandy Don]
-
A.
Dandy Don
chosen
Dandy Don was the popular nickname of Don Meredith, a star Dallas Cowboys quarterback and pioneering color commentator on Monday Night Football.
-
B.
Dandy Dan
Dandy Dan is the sharply dressed, ruthless mob boss antagonist in the 1976 musical gangster film "Bugsy Malone."
-
C.
Dum Dum Dugan
Dum Dum Dugan is a gruff, mustachioed World War II-era soldier and close ally of Nick Fury in Marvel Comics, renowned for his combat skills and leadership within elite military units.
-
D.
Eldee the Don
Eldee the Don is a Nigerian rapper, producer, and pioneer of the Afrobeats and hip-hop scene, known for his influential work both as a solo artist and as a member of the group Trybesmen.
-
E.
Da-Dandy
Da-Dandy is a photomontage artwork by German Dada artist Hannah Höch that critiques gender roles and Weimar-era modernity through fragmented, collage-based imagery.
- 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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e25e2d08108190bab1b3325923af1d |
completed | April 17, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001f9965b8819080278ccef15288aa |
completed | May 10, 2026, 6:03 a.m. |
Created at: April 10, 2026, 5:06 a.m.