Triple
T14488706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khor Al Beidah |
E359305
|
entity |
| Predicate | hasFloraCommonName |
P77570
|
FINISHED |
| Object | grey mangrove |
—
|
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: grey mangrove | Statement: [Khor Al Beidah, hasFloraCommonName, grey mangrove]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFloraCommonName Context triple: [Khor Al Beidah, hasFloraCommonName, grey mangrove]
-
A.
includesSpeciesCommonName
chosen
Indicates that an entity contains or specifies the common (vernacular) name of a species.
-
B.
associatedFlora
Indicates a relationship where specific plants or vegetation are characteristically linked to, occur with, or are commonly found in association with a given entity or environment.
-
C.
hasFloraGroup
Indicates that an entity is associated with, contains, or is characterized by a particular group or category of plant life.
-
D.
commonNameOfNotableSpecies
Indicates that the subject is a commonly used vernacular or everyday name for a notable or well-known biological species.
-
E.
commonNameOfGenus
Indicates that the object is a commonly used name referring to the genus specified by the subject.
- 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_69d8279740308190af9df93a3af8592e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de930bd1d48190abd6c47da0a3ebc8 |
completed | April 14, 2026, 7:18 p.m. |
| PD | Predicate disambiguation | batch_69de5c487b4c819097803e58dca628a5 |
completed | April 14, 2026, 3:24 p.m. |
Created at: April 10, 2026, 1:20 a.m.