Triple
T37887695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doc Daneeka |
E945042
|
entity |
| Predicate | relationshipToYossarian |
P204450
|
FINISHED |
| Object | squadron flight surgeon |
—
|
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: squadron flight surgeon | Statement: [Doc Daneeka, relationshipToYossarian, squadron flight surgeon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToYossarian Context triple: [Doc Daneeka, relationshipToYossarian, squadron flight surgeon]
-
A.
relationshipToTheDude
Indicates the specific type of personal or social relationship that one entity has to the individual referred to as "the Dude."
-
B.
relationshipToLeopoldBloom
Indicates the specific type of personal or social relationship an entity has with Leopold Bloom.
-
C.
relationToBenitoCereno
Indicates the specific relationship or connection an entity has to the character or figure Benito Cereno.
-
D.
relationshipToHeed
Indicates a relationship in which one entity is expected to pay attention to, respect, or follow the guidance, warnings, or wishes of another entity.
-
E.
relationshipToRyanBingham
Indicates the specific type of personal or social relationship an entity has with Ryan Bingham.
- 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_69f76ef02668819089e7940c4001af5e |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a192a008190a9917688a9e804f4 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:19 p.m.