Triple
T14613142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gruesome Playground Injuries |
E343008
|
entity |
| Predicate | hasProtagonist |
P8706
|
FINISHED |
| Object | Doug |
E1851
|
NE 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: Doug | Statement: [Gruesome Playground Injuries, hasProtagonist, Doug]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Doug Context triple: [Gruesome Playground Injuries, hasProtagonist, Doug]
-
A.
Doug
chosen
Doug is a common English masculine given name, typically used as a short form of Douglas.
-
B.
Dave
Dave is a common masculine given name, often a shortened form of David, used widely in English-speaking countries.
-
C.
Don
The Don is a major river in southwestern Russia that flows from the Central Russian Upland to the Sea of Azov, historically serving as an important trade route and cultural boundary.
-
D.
Don
Don is a masculine given name, often a short form of Donald, used in English-speaking countries.
-
E.
Don
Don is a classic 1978 Bollywood action-thriller film, starring Amitabh Bachchan in a dual role, that became iconic for its stylish crime narrative, memorable music, and enduring cultural impact.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb450e6588190a94488d8e71888c8 |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fda92110e88190af47b713dd24520b |
completed | May 8, 2026, 9:13 a.m. |
Created at: April 10, 2026, 1:25 a.m.