Triple
T38518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lee |
E762
|
entity |
| Predicate | isAmbiguousWith |
P2289
|
FINISHED |
| Object | multiple notable people named Lee |
—
|
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: multiple notable people named Lee | Statement: [Lee, isAmbiguousWith, multiple notable people named Lee]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isAmbiguousWith Context triple: [Lee, isAmbiguousWith, multiple notable people named Lee]
-
A.
isDistinctFrom
Indicates that two entities are not identical and can be clearly distinguished from one another.
-
B.
overlapsWith
Indicates that two entities share a common part or region in space, time, or extent, but neither is completely contained within the other.
-
C.
oftenConfusedWith
chosen
Indicates that one entity is frequently mistaken for or thought to be another due to similarity or ambiguity.
-
D.
competesWith
Indicates that two entities are in rivalry or opposition, each striving to outperform or gain advantage over the other in the same domain or objective.
-
E.
isComparedTo
Indicates that one entity is evaluated or measured in relation to another to highlight similarities, differences, or relative qualities.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24b4d5bd08190a3a48eb26e67768c |
completed | Feb. 28, 2026, 1:56 a.m. |
| PD | Predicate disambiguation | batch_69a24ab6141881908701106aa97e4735 |
completed | Feb. 28, 2026, 1:53 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.