Triple
T138366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Painted Ladies (San Francisco) |
E2796
|
entity |
| Predicate | termCoinedIn |
P2778
|
FINISHED |
| Object | 1978 |
—
|
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: 1978 | Statement: [Painted Ladies (San Francisco), termCoinedIn, 1978]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: termCoinedIn Context triple: [Painted Ladies (San Francisco), termCoinedIn, 1978]
-
A.
coinedTerm
Indicates that an entity originated and introduced a particular term or expression into use.
-
B.
coinedIn
chosen
Indicates that something (typically a term, phrase, or name) was first created or introduced at a particular time or in a particular place.
-
C.
keyTerm
Indicates that a term functions as a primary or central concept within a given context or information structure.
-
D.
etymologyType
Indicates the specific kind or category of etymological relationship that links a term to its linguistic origin or source.
-
E.
etymology
Indicates the historical origin and development of a word or term, including its source language and form.
- 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_69a2521e35c08190b28e5c9f1e3c9b59 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a257a800148190be119d1d075869b8 |
completed | Feb. 28, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69a25652efdc8190b85b33735a9e6370 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.