Triple
T52801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chrissy Teigen |
E1036
|
entity |
| Predicate | hasSpokenAbout |
P3281
|
FINISHED |
| Object | postpartum depression |
—
|
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: postpartum depression | Statement: [Chrissy Teigen, hasSpokenAbout, postpartum depression]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpokenAbout Context triple: [Chrissy Teigen, hasSpokenAbout, postpartum depression]
-
A.
hasPerson
Indicates that an entity is associated with or includes a specific person.
-
B.
hasNotableSubject
Indicates that an entity is associated with a subject that is particularly significant, prominent, or noteworthy in relation to it.
-
C.
introducedTo
Indicates that one entity caused or facilitated a first meeting or formal presentation between another entity and a third party.
-
D.
languagesSpoken
Indicates that an entity is able to communicate using one or more specified languages.
-
E.
hasReception
Indicates that an entity hosts, includes, or is associated with a reception event (such as a formal gathering or welcoming function).
- 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_69a2480baefc81909951b14058479aa2 |
completed | Feb. 28, 2026, 1:42 a.m. |
| NER | Named-entity recognition | batch_69a24c709c248190bcd442c8d508e48c |
completed | Feb. 28, 2026, 2:01 a.m. |
| PD | Predicate disambiguation | batch_69a24ac3c8dc819099849023bdaa35a9 |
completed | Feb. 28, 2026, 1:54 a.m. |
| PDg | Predicate description generation | batch_69a24c6ff0588190b0fd864da9aa8569 |
completed | Feb. 28, 2026, 2:01 a.m. |
Created at: Feb. 28, 2026, 1:47 a.m.