Triple
T13778187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hive Mind |
E331064
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Christopher Smith |
E338926
|
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: Christopher Smith | Statement: [Hive Mind, producer, Christopher Smith]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Christopher Smith Context triple: [Hive Mind, producer, Christopher Smith]
-
A.
Christopher Smith
chosen
Christopher Smith is an individual associated with online communities or organizations connected to the Internet.
-
B.
Christopher Smith
Christopher Smith is a relative of English actor Jeremy Irvine, known for his role in the film "War Horse."
-
C.
Christopher Smith
Christopher Smith is an actor known for his performance in the film "Feel Good."
-
D.
Leo Smith
Leo Smith is an American avant-garde jazz trumpeter and composer known for his innovative contributions to creative and experimental music.
-
E.
Leo Smith
Leo Smith is an education administrator who serves as the business administrator for the Bayonne School District, overseeing its financial and operational affairs.
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0239cbfc81909064ac2457fdfff5 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b8d4af2081909628c1691e073f6f |
completed | May 3, 2026, 9:06 p.m. |
Created at: April 9, 2026, 10:11 p.m.