Triple
T10198266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jenny |
E238818
|
entity |
| Predicate | partnerOf |
P1136
|
FINISHED |
| Object | Charlie |
unclear NED1
|
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: Charlie | Statement: [Jenny, partnerOf, Charlie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charlie Context triple: [Jenny, partnerOf, Charlie]
-
A.
Charlie
Charlie is the fictional Boston subway rider in the folk song "Charlie on the MTA," known for being unable to get off the train because he lacks the fare to exit.
-
B.
Charlie
Charlie is Dory’s loving but forgetful father in the animated film "Finding Dory," known for his patience, optimism, and inventive ways of helping her cope with memory loss.
-
C.
Charlie
Charlie is the given name of the British philosopher and Cambridge academic C. D. Broad.
-
D.
Charlie
Charlie is the yellow-suited captain and one of the three main playable leaders in Pikmin 3, known for commanding Pikmin on the planet PNF-404.
-
E.
Charlie
Charlie is a central character in the romantic comedy film "French Kiss," serving as the unfaithful fiancé whose actions set the story’s events in motion.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69ca84e1ea088190b38162e43d4cfa8f |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdee3c44408190b09fa41f2d257c04 |
completed | April 2, 2026, 4:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d32aef701c8190a01e632eb4fda1b9 |
completed | April 6, 2026, 3:39 a.m. |
Created at: March 30, 2026, 9:14 p.m.