Triple
T22283373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hermann Trophy |
E550795
|
entity |
| Predicate | firstWomenAwarded |
P147682
|
FINISHED |
| Object | 1988 |
—
|
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: 1988 | Statement: [Hermann Trophy, firstWomenAwarded, 1988]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstWomenAwarded Context triple: [Hermann Trophy, firstWomenAwarded, 1988]
-
A.
notableFemaleWinner
Indicates that the subject is a female who has achieved a notable or distinguished victory in the specified context.
-
B.
numberOfHonoredWomen
Indicates the count of women who have been honored or recognized in a given context or event.
-
C.
admittedWomen
Indicates that an entity allowed or accepted women into a place, group, institution, or event.
-
D.
notableFemaleMember
Indicates that an entity has a female member who is particularly prominent, distinguished, or noteworthy within that entity.
-
E.
hasFirstFemaleGraduate
Indicates that an institution or program has a specific person who is recognized as its first female graduate.
- 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_69e11e44d538819097c6b8f333af3352 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f14eacd6cc8190812c6f672641050e |
completed | April 29, 2026, 12:19 a.m. |
| PD | Predicate disambiguation | batch_69e72ff0363081909f794d19c8a64837 |
completed | April 21, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e7342ce08c8190bc0a7085f4a952e7 |
completed | April 21, 2026, 8:24 a.m. |
Created at: April 16, 2026, 8:40 p.m.