Triple
T553099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Holzman |
E11883
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Red |
E63964
|
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: Red | Statement: [Red Holzman, nickname, Red]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Red Context triple: [Red Holzman, nickname, Red]
-
A.
Red
chosen
Red is the famous nickname of Arnold "Red" Auerbach, the legendary Boston Celtics coach and executive known for his pivotal role in building an NBA dynasty.
-
B.
Crimson
Crimson is the collective name for Harvard University's varsity athletic teams competing in collegiate sports.
-
C.
Reddish
Reddish is a suburban area and former industrial village in the Metropolitan Borough of Stockport, Greater Manchester, England.
-
D.
Orange
Orange is a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
-
E.
Black-and-Red
Black-and-Red is the widely used nickname for Major League Soccer club D.C. United, referencing the team’s traditional colors and identity.
- 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_69a4932941d08190815efd422f0b4ca7 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4991b296481908cf27e1d1ec67052 |
completed | March 1, 2026, 7:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4e3f90058819081167bac387f8023 |
completed | March 2, 2026, 1:12 a.m. |
Created at: March 1, 2026, 7:32 p.m.