Triple
T7904369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lieutenant Dan Taylor |
E183533
|
entity |
| Predicate | receivesAward |
P45356
|
FINISHED |
| Object | Medal for military service (implied in film’s context) |
—
|
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: Medal for military service (implied in film’s context) | Statement: [Lieutenant Dan Taylor, receivesAward, Medal for military service (implied in film’s context)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: receivesAward Context triple: [Lieutenant Dan Taylor, receivesAward, Medal for military service (implied in film’s context)]
-
A.
awardReceived
Indicates that an entity has been granted or honored with a specific award or recognition.
-
B.
awardReceivedWith
Indicates that an entity received a specific award, optionally together with additional contextual details such as the work, role, or circumstances associated with that award.
-
C.
recipientOfAward
chosen
Indicates that an entity has received or been granted a particular award.
-
D.
awardFor
Indicates that something is given or granted as recognition or a prize for a particular achievement, work, or contribution.
-
E.
awardConferred
Indicates that an award or honor has been formally granted by one entity to another.
- 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_69ca828d13088190b222be7aa9f9315c |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a4331cc8190b50301c78767a850 |
completed | March 31, 2026, 3:06 a.m. |
| PD | Predicate disambiguation | batch_69cae92f9498819085277879e59aa072 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:02 p.m.