Triple
T26116841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Brow |
E658845
|
entity |
| Predicate | refersToPhysicalFeature |
P102056
|
FINISHED |
| Object | unibrow |
—
|
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: unibrow | Statement: [The Brow, refersToPhysicalFeature, unibrow]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: refersToPhysicalFeature Context triple: [The Brow, refersToPhysicalFeature, unibrow]
-
A.
refersToGeographicFeature
Indicates that one entity makes reference to, denotes, or is associated with a specific geographic feature such as a landform, body of water, or other physical location.
-
B.
refersToNaturalWonder
Indicates that one entity makes reference to, mentions, or points specifically to a natural wonder (such as a notable natural landmark or formation).
-
C.
refersToUrbanFeature
Indicates that one entity makes reference or points specifically to an urban feature such as a city-related structure, space, or infrastructure element.
-
D.
refersToLocation
Indicates that one entity designates, points to, or identifies a specific location associated with it.
-
E.
physicalFeature
chosen
Indicates that one entity possesses or exhibits a particular physical characteristic or attribute.
- 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_69ee5bc20298819099a42be042eb2349 |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f66c5c13808190887180099745673b |
completed | May 2, 2026, 9:27 p.m. |
| PD | Predicate disambiguation | batch_69f66abddc448190a488852f8abdeb2c |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 26, 2026, 8:05 p.m.