Triple
T509517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lou Gehrig |
E10573
|
entity |
| Predicate | knownForEvent |
P22
|
FINISHED |
| Object | farewell speech at Yankee Stadium |
—
|
LITERAL 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: farewell speech at Yankee Stadium | Statement: [Lou Gehrig, knownForEvent, farewell speech at Yankee Stadium]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: knownForEvent Context triple: [Lou Gehrig, knownForEvent, farewell speech at Yankee Stadium]
-
A.
knownForSports
Indicates that an entity is recognized or notable for its involvement, achievement, or association with sports.
-
B.
notableFor
chosen
Indicates that an entity is especially recognized or distinguished for a particular quality, achievement, characteristic, or role.
-
C.
underlyingKnownFor
Indicates that one entity is fundamentally or primarily recognized as the basis or main reason for another entity’s notability or fame.
-
D.
knownFrom
Indicates that one entity is aware of, has learned about, or recognizes another entity through a specified source, context, or medium.
-
E.
featuresEvent
Indicates that an entity includes, presents, or highlights a particular event as part of its content or offering.
- F. None of above.
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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f164a9d48190b525a97b5c06ffe2 |
completed | Feb. 28, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69a2edfe236481909901cc7d4281b33c |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.