Triple
T247765
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | F |
E5075
|
entity |
| Predicate | underlyingKnownFor |
P8759
|
FINISHED |
| Object | mass production of automobiles |
—
|
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: mass production of automobiles | Statement: [F, underlyingKnownFor, mass production of automobiles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: underlyingKnownFor Context triple: [F, underlyingKnownFor, mass production of automobiles]
-
A.
alsoKnownAs
Indicates that one entity is an alternative name, alias, or designation for another entity.
-
B.
knownFrom
Indicates that one entity is aware of, has learned about, or recognizes another entity through a specified source, context, or medium.
-
C.
notableStar
Indicates that the subject is a star (or stellar object) that is distinguished or noteworthy in some significant way, such as brightness, fame, or scientific interest, relative to other stars.
-
D.
starredActor
Indicates that an actor performed a leading or significant role in a particular production or work.
-
E.
knownForSports
Indicates that an entity is recognized or notable for its involvement, achievement, or association with sports.
- 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_69a257c4bf688190a46ebbf411ab7473 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d154ebc819087a5c9dc4f62ff44 |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b64ea3081908de626a0e1445bdb |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25c2ca46c81908c61696f31e59a98 |
completed | Feb. 28, 2026, 3:08 a.m. |
Created at: Feb. 28, 2026, 2:54 a.m.