Triple
T7171070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lehigh University |
E167195
|
entity |
| Predicate | formerAthleticsNickname |
P74525
|
FINISHED |
| Object | Engineers |
—
|
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: Engineers | Statement: [Lehigh University, formerAthleticsNickname, Engineers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: formerAthleticsNickname Context triple: [Lehigh University, formerAthleticsNickname, Engineers]
-
A.
athleticsNickname
Indicates the commonly used nickname or moniker for an entity’s athletics team or sports program.
-
B.
nicknamedFor
Indicates that one entity serves as the source, inspiration, or reason for another entity’s nickname.
-
C.
runnerUpNickname
Indicates that one entity is the nickname used for the runner-up position or second-place finisher in relation to another entity.
-
D.
stageNickname
Indicates that one entity is the stage name or performance nickname used by another entity.
-
E.
formerSport
Indicates that an entity previously played or participated in a particular sport but no longer does so.
- F. None of above. chosen
Provenance (4 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_69c68889a2748190a316c5e65360361a |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e85ec718819085af59fadee9d22d |
completed | March 27, 2026, 8:28 p.m. |
| PD | Predicate disambiguation | batch_69c6e1ce5f048190808c378a980f7bb6 |
completed | March 27, 2026, 8 p.m. |
| PDg | Predicate description generation | batch_69c6e24af9ac8190b24eee206ba8be36 |
completed | March 27, 2026, 8:02 p.m. |
Created at: March 27, 2026, 2:48 p.m.