Triple
T38649028
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | actor-network theory |
E938791
|
entity |
| Predicate | treatsAsActors |
P112887
|
FINISHED |
| Object | humans |
—
|
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: humans | Statement: [actor-network theory, treatsAsActors, humans]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: treatsAsActors Context triple: [actor-network theory, treatsAsActors, humans]
-
A.
supportedActor
Indicates that one entity provided assistance, resources, or endorsement to another entity (the supported actor).
-
B.
actsFor
Indicates that one entity performs actions or exercises authority on behalf of another entity.
-
C.
marksReturnOfActorAsCharacter
Indicates that an instance marks the return of an actor portraying a particular character after a period of absence.
-
D.
treatsAsSubject
chosen
Indicates that one entity regards, handles, or processes another entity in the role or capacity of a subject (e.g., topic, focus, or primary object of consideration).
-
E.
playsAs
Indicates that one entity performs, portrays, or assumes the role, character, or persona of another entity.
- 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_69f76ed948ec81908ce7811608a8f359 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcf825ca7081909d06b0df33eb33f9 |
completed | May 7, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69fcf42160f0819096812a8bf590875e |
completed | May 7, 2026, 8:20 p.m. |
Created at: May 3, 2026, 4:32 p.m.